Low complexity random codebook structure

ABSTRACT

A multi-rate speech codec supports a plurality of encoding bit rate modes by adaptively selecting encoding bit rate modes to match communication channel restrictions. In higher bit rate encoding modes, an accurate representation of speech through CELP (code excited linear prediction) and other associated modeling parameters are generated for higher quality decoding and reproduction. To achieve high quality in lower bit rate encoding modes, the speech encoder departs from the strict waveform matching criteria of regular CELP coders and strives to identify significant perceptual features of the input signal. The encoder generates pluralities of codevectors from a single, normalized codevector by shifting or other rearrangement. As a result, searching speeds are enhanced, and the physical size of a codebook built from such codevectors is greatly reduced.

BACKGROUND OF THE INVENTION

[0001] 1. Technical Field

[0002] The present invention relates generally to speech encoding anddecoding in voice communication systems; and, more particularly, itrelates to various techniques used with code-excited linear predictioncoding to obtain high quality speech reproduction through a limited bitrate communication channel.

[0003] 2. Related Art

[0004] Signal modeling and parameter estimation play significant rolesin communicating voice information with limited bandwidth constraints.To model basic speech sounds, speech signals are sampled as a discretewaveform to be digitally processed. In one type of signal codingtechnique called LPC (linear predictive coding), the signal value at anyparticular time index is modeled as a linear function of previousvalues. A subsequent signal is thus linearly predictable according to anearlier value. As a result, efficient signal representations can bedetermined by estimating and applying certain prediction parameters torepresent the signal.

[0005] Applying LPC techniques, a conventional source encoder operateson speech signals to extract modeling and parameter information forcommunication to a conventional source decoder via a communicationchannel. Once received, the decoder attempts to reconstruct acounterpart signal for playback that sounds to a human ear like theoriginal speech.

[0006] A certain amount of communication channel bandwidth is requiredto communicate the modeling and parameter information to the decoder. Inembodiments, for example where the channel bandwidth is shared andreal-time reconstruction is necessary, a reduction in the requiredbandwidth proves beneficial. However, using conventional modelingtechniques, the quality requirements in the reproduced speech limit thereduction of such bandwidth below certain levels.

[0007] Speech encoding becomes increasingly difficult as transmissionbit rates decrease. Particularly for noise encoding, perceptual qualitydiminishes significantly at lower bit rates. Straightforwardcode-excited linear prediction (CELP) is used in many speech codecs, andit can be very effective method of encoding speech at relatively hightransmission rates. However, even this method may fail to provideperceptually accurate signal reproduction at lower bit rates. One suchreason is that the pulse like excitation for noise signals becomes moresparse at these lower bit rates as less bits are available for codingand transmission, thereby resulting in annoying distortion of the noisesignal upon reproduction.

[0008] Many communication systems operate at bit rates that vary withany number of factors including total traffic on the communicationsystem. For such variable rate communication systems, the inability todetect low bit rates and to handle the coding of noise at those lowerbit rates in an effective manner often can result in perceptuallyinaccurate reproduction of the speech signal. This inaccuratereproduction could be avoided if a more effective method for encodingnoise at those low bit rates were identified.

[0009] Additionally, the inability to determine the optimal encodingmode for a given noise signal at a given bit rate also results in aninefficient use of encoding resources. For a given speech signal havinga particular noise component, the ability to selectively apply anoptimal coding scheme at a given bit rate would provide more efficientuse of an encoder processing circuit. Moreover, the ability to selectthe optimal encoding mode for type of noise signal would furthermaximize the available encoding resources while providing a moreperceptually accurate reproduction of the noise signal.

SUMMARY OF THE INVENTION

[0010] A random codebook is implemented utilizing overlap in order toreduce storage space. This arrangement necessitates reference to a tableor other index that lists the energies for each codebook vector.Accordingly, the table or other index, and the respective energy values,must be stored, thereby adding computational and storage complexity tosuch a system.

[0011] The present invention re-uses each table codevector entry in arandom table with “L” codevectors, each of dimension “N.” That is, forexample, an exemplary codebook contains codevectors V₀, V₁, . . . ,V_(L), with each codevector V_(x) being of dimension N, and having bitsC₀, C₁, . . . , C_(N-1), C_(N). Each codevector of dimension N isnormalized to an energy value of unity, thereby reducing computationalcomplexity to a minimum.

[0012] Each codebook entry essentially acts as a circular buffer wherebyN different random codebook vectors are generated by specifying astarting point at each different bit in a given codevector. Each of thedifferent N codevectors then has unity energy.

[0013] The dimension of each table entry is identical to the dimensionof the required random codevector and every element in a particulartable entry will be in any codevector derived from this table entry.This arrangement dramatically reduces the necessary storage capacity ofa given system, while maintaining minimal computational complexity.

[0014] Other aspects, advantages and novel features of the presentinvention will become apparent from the following detailed descriptionof the invention when considered in conjunction with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015]FIG. 1a is a schematic block diagram of a speech communicationsystem illustrating the use of source encoding and decoding inaccordance with the present invention.

[0016]FIG. 1b is a schematic block diagram illustrating an exemplarycommunication device utilizing the source encoding and decodingfunctionality of FIG. 1a.

[0017] FIGS. 2-4 are functional block diagrams illustrating a multi-stepencoding approach used by one embodiment of the speech encoderillustrated in FIGS. 1a and 1 b. In particular, FIG. 2 is a functionalblock diagram illustrating of a first stage of operations performed byone embodiment of the speech encoder of FIGS. 1a and 1 b. FIG. 3 is afunctional block diagram of a second stage of operations, while FIG. 4illustrates a third stage.

[0018]FIG. 5 is a block diagram of one embodiment of the speech decodershown in FIGS. 1a and 1 b having corresponding functionality to thatillustrated in FIGS. 2-4.

[0019]FIG. 6 is a block diagram of an alternate embodiment of a speechencoder that is built in accordance with the present invention.

[0020]FIG. 7 is a block diagram of an embodiment of a speech decoderhaving corresponding functionality to that of the speech encoder of FIG.6.

[0021]FIG. 8 is a block diagram of the low complexity codebook structurein accordance with the present invention.

[0022]FIG. 9 is a block diagram of the low complexity codebook structureof the present invention that demonstrates that the table entries can beshifted in increments of two or more entries at a time.

[0023]FIG. 10 is a block diagram of the low complexity codebook of thepresent invention that demonstrates that the given codevectors can bepseudo-randomly repopulated with entries 0 through N.

DETAILED DESCRIPTION

[0024]FIG. 1a is a schematic block diagram of a speech communicationsystem illustrating the use of source encoding and decoding inaccordance with the present invention. Therein, a speech communicationsystem 100 supports communication and reproduction of speech across acommunication channel 103. Although it may comprise for example a wire,fiber or optical link, the communication channel 103 typicallycomprises, at least in part, a radio frequency link that often mustsupport multiple, simultaneous speech exchanges requiring sharedbandwidth resources such as may be found with cellular telephonyembodiments.

[0025] Although not shown, a storage device may be coupled to thecommunication channel 103 to temporarily store speech information fordelayed reproduction or playback, e.g., to perform answering machinefunctionality, voiced email, etc. Likewise, the communication channel103 might be replaced by such a storage device in a single deviceembodiment of the communication system 100 that, for example, merelyrecords and stores speech for subsequent playback.

[0026] In particular, a microphone 111 produces a speech signal in realtime. The microphone 111 delivers the speech signal to an A/D (analog todigital) converter 115. The AID converter 115 converts the speech signalto a digital form then delivers the digitized speech signal to a speechencoder 117.

[0027] The speech encoder 117 encodes the digitized speech by using aselected one of a plurality of encoding modes. Each of the plurality ofencoding modes utilizes particular techniques that attempt to optimizequality of resultant reproduced speech. While operating in any of theplurality of modes, the speech encoder 117 produces a series of modelingand parameter information (hereinafter “speech indices”), and deliversthe speech indices to a channel encoder 119.

[0028] The channel encoder 119 coordinates with a channel decoder 131 todeliver the speech indices across the communication channel 103. Thechannel decoder 131 forwards the speech indices to a speech decoder 133.While operating in a mode that corresponds to that of the speech encoder117, the speech decoder 133 attempts to recreate the original speechfrom the speech indices as accurately as possible at a speaker 137 via aD/A (digital to analog) converter 135.

[0029] The speech encoder 117 adaptively selects one of the plurality ofoperating modes based on the data rate restrictions through thecommunication channel 103. The communication channel 103 comprises abandwidth allocation between the channel encoder 119 and the channeldecoder 131. The allocation is established, for example, by telephoneswitching networks wherein many such channels are allocated andreallocated as need arises. In one such embodiment, either a 22.8 kbps(kilobits per second) channel bandwidth, i.e., a full rate channel, or a11.4 kbps channel bandwidth, i.e., a half rate channel, may beallocated.

[0030] With the full rate channel bandwidth allocation, the speechencoder 117 may adaptively select an encoding mode that supports a bitrate of 11.0, 8.0, 6.65 or 5.8 kbps. The speech encoder 117 adaptivelyselects an either 8.0, 6.65, 5.8 or 4.5 kbps encoding bit rate mode whenonly the half rate channel has been allocated. Of course these encodingbit rates and the aforementioned channel allocations are onlyrepresentative of the present embodiment. Other variations to meet thegoals of alternate embodiments are contemplated.

[0031] With either the full or half rate allocation, the speech encoder117 attempts to communicate using the highest encoding bit rate modethat the allocated channel will support. If the allocated channel is orbecomes noisy or otherwise restrictive to the highest or higher encodingbit rates, the speech encoder 117 adapts by selecting a lower bit rateencoding mode. Similarly, when the communication channel 103 becomesmore favorable, the speech encoder 117 adapts by switching to a higherbit rate encoding mode.

[0032] With lower bit rate encoding, the speech encoder 117 incorporatesvarious techniques to generate better low bit rate speech reproduction.Many of the techniques applied are based on characteristics of thespeech itself. For example, with lower bit rate encoding, the speechencoder 117 classifies noise, unvoiced speech, and voiced speech so thatan appropriate modeling scheme corresponding to a particularclassification can be selected and implemented. Thus, the speech encoder117 adaptively selects from among a plurality of modeling schemes thosemost suited for the current speech. The speech encoder 117 also appliesvarious other techniques to optimize the modeling as set forth in moredetail below.

[0033]FIG. 1b is a schematic block diagram illustrating severalvariations of an exemplary communication device employing thefunctionality of FIG. 1a. A communication device 151 comprises both aspeech encoder and decoder for simultaneous capture and reproduction ofspeech. Typically within a single housing, the communication device 151might, for example, comprise a cellular telephone, portable telephone,computing system, etc. Alternatively, with some modification to includefor example a memory element to store encoded speech information thecommunication device 151 might comprise an answering machine, arecorder, voice mail system, etc.

[0034] A microphone 155 and an A/D converter 157 coordinate to deliver adigital voice signal to an encoding system 159. The encoding system 159performs speech and channel encoding and delivers resultant speechinformation to the channel. The delivered speech information may bedestined for another communication device ( not shown) at a remotelocation.

[0035] As speech information is received, a decoding system 165 performschannel and speech decoding then coordinates with a D/A converter 167and a speaker 169 to reproduce something that sounds like the originallycaptured speech.

[0036] The encoding system 159 comprises both a speech processingcircuit 185 that performs speech encoding, and a channel processingcircuit 187 that performs channel encoding. Similarly, the decodingsystem 165 comprises a speech processing circuit 189 that performsspeech decoding, and a channel processing circuit 191 that performschannel decoding.

[0037] Although the speech processing circuit 185 and the channelprocessing circuit 187 are separately illustrated, they might becombined in part or in total into a single unit. For example, the speechprocessing circuit 185 and the channel processing circuitry 187 mightshare a single DSP (digital signal processor) and/or other processingcircuitry. Similarly, the speech processing circuit 189 and the channelprocessing circuit 191 might be entirely separate or combined in part orin whole. Moreover, combinations in whole or in part might be applied tothe speech processing circuits 185 and 189, the channel processingcircuits 187 and 191, the processing circuits 185, 187, 189 and 191, orotherwise.

[0038] The encoding system 159 and the decoding system 165 both utilizea memory 161. The speech processing circuit 185 utilizes a fixedcodebook 181 and an adaptive codebook 183 of a speech memory 177 in thesource encoding process. The channel processing circuit 187 utilizes achannel memory 175 to perform channel encoding. Similarly, the speechprocessing circuit 189 utilizes the fixed codebook 181 and the adaptivecodebook 183 in the source decoding process. The channel processingcircuit 187 utilizes the channel memory 175 to perform channel decoding.

[0039] Although the speech memory 177 is shared as illustrated, separatecopies thereof can be assigned for the processing circuits 185 and 189.Likewise, separate channel memory can be allocated to both theprocessing circuits 187 and 191. The memory 161 also contains softwareutilized by the processing circuits 185,187,189 and 191 to performvarious functionality required in the source and channel encoding anddecoding processes.

[0040] FIGS. 2-4 are functional block diagrams illustrating a multi-stepencoding approach used by one embodiment of the speech encoderillustrated in FIGS. 1a and 1 b. In particular, FIG. 2 is a functionalblock diagram illustrating of a first stage of operations performed byone embodiment of the speech encoder shown in FIGS. 1a and 1 b. Thespeech encoder, which comprises encoder processing circuitry, typicallyoperates pursuant to software instruction carrying out the followingfunctionality.

[0041] At a block 215, source encoder processing circuitry performs highpass filtering of a speech signal 211. The filter uses a cutofffrequency of around 80 Hz to remove, for example, 60 Hz power line noiseand other lower frequency signals. After such filtering, the sourceencoder processing circuitry applies a perceptual weighting filter asrepresented by a block 219. The perceptual weighting filter operates toemphasize the valley areas of the filtered speech signal.

[0042] If the encoder processing circuitry selects operation in a pitchpreprocessing (PP) mode as indicated at a control block 245, a pitchpreprocessing operation is performed on the weighted speech signal at ablock 225. The pitch preprocessing operation involves warping theweighted speech signal to match interpolated pitch values that will begenerated by the decoder processing circuitry. When pitch preprocessingis applied, the warped speech signal is designated a first target signal229. If pitch preprocessing is not selected the control block 245, theweighted speech signal passes through the block 225 without pitchpreprocessing and is designated the first target signal 229.

[0043] As represented by a block 255, the encoder processing circuitryapplies a process wherein a contribution from an adaptive codebook 257is selected along with a corresponding gain 257 which minimize a firsterror signal 253. The first error signal 253 comprises the differencebetween the first target signal 229 and a weighted, synthesizedcontribution from the adaptive codebook 257.

[0044] At blocks 247, 249 and 251, the resultant excitation vector isapplied after adaptive gain reduction to both a synthesis and aweighting filter to generate a modeled signal that best matches thefirst target signal 229. The encoder processing circuitry uses LPC(linear predictive coding) analysis, as indicated by a block 239, togenerate filter parameters for the synthesis and weighting filters. Theweighting filters 219 and 251 are equivalent in functionality.

[0045] Next, the encoder processing circuitry designates the first errorsignal 253 as a second target signal for matching using contributionsfrom a fixed codebook 261. The encoder processing circuitry searchesthrough at least one of the plurality of subcodebooks within the fixedcodebook 261 in an attempt to select a most appropriate contributionwhile generally attempting to match the second target signal.

[0046] More specifically, the encoder processing circuitry selects anexcitation vector, its corresponding subcodebook and gain based on avariety of factors. For example, the encoding bit rate, the degree ofminimization, and characteristics of the speech itself as represented bya block 279 are considered by the encoder processing circuitry atcontrol block 275. Although many other factors may be considered,exemplary characteristics include speech classification, noise level,sharpness, periodicity, etc. Thus, by considering other such factors, afirst subcodebook with its best excitation vector may be selected ratherthan a second subcodebook's best excitation vector even though thesecond subcodebook's better minimizes the second target signal 265.

[0047]FIG. 3 is a functional block diagram depicting of a second stageof operations performed by the embodiment of the speech encoderillustrated in FIG. 2. In the second stage, the speech encodingcircuitry simultaneously uses both the adaptive the fixed codebookvectors found in the first stage of operations to minimize a third errorsignal 311.

[0048] The speech encoding circuitry searches for optimum gain valuesfor the previously identified excitation vectors ( in the first stage)from both the adaptive and fixed codebooks 257 and 261. As indicated byblocks 307 and 309, the speech encoding circuitry identifies the optimumgain by generating a synthesized and weighted signal, i.e., via a block301 and 303, that best matches the first target signal 229 (whichminimizes the third error signal 311). Of course if processingcapabilities permit, the first and second stages could be combinedwherein joint optimization of both gain and adaptive and fixed codebookrector selection could be used.

[0049]FIG. 4 is a functional block diagram depicting of a third stage ofoperations performed by the embodiment of the speech encoder illustratedin FIGS. 2 and 3. The encoder processing circuitry applies gainnormalization, smoothing and quantization, as represented by blocks 401,403 and 405, respectively, to the jointly optimized gains identified inthe second stage of encoder processing. Again, the adaptive and fixedcodebook vectors used are those identified in the first stageprocessing.

[0050] With normalization, smoothing and quantization functionallyapplied, the encoder processing circuitry has completed the modelingprocess. Therefore, the modeling parameters identified are communicatedto the decoder. In particular, the encoder processing circuitry deliversan index to the selected adaptive codebook vector to the channel encodervia a multiplexor 419. Similarly, the encoder processing circuitrydelivers the index to the selected fixed codebook vector, resultantgains, synthesis filter parameters, etc., to the muliplexor 419. Themultiplexor 419 generates a bit stream 421 of such information fordelivery to the channel encoder for communication to the channel andspeech decoder of receiving device.

[0051]FIG. 5 is a block diagram of an embodiment illustratingfunctionality of speech decoder having corresponding functionality tothat illustrated in FIGS. 2-4. As with the speech encoder, the speechdecoder, which comprises decoder processing circuitry, typicallyoperates pursuant to software instruction carrying out the followingfunctionality.

[0052] A demultiplexor 511 receives a bit stream 513 of speech modelingindices from an often remote encoder via a channel decoder. Aspreviously discussed, the encoder selected each index value during themulti-stage encoding process described above in reference to FIGS. 2-4.The decoder processing circuitry utilizes indices, for example, toselect excitation vectors from an adaptive codebook 515 and a fixedcodebook 519, set the adaptive and fixed codebook gains at a block 521,and set the parameters for a synthesis filter 531.

[0053] With such parameters and vectors selected or set, the decoderprocessing circuitry generates a reproduced speech signal 539. Inparticular, the codebooks 515 and 519 generate excitation vectorsidentified by the indices from the demultiplexor 511. The decoderprocessing circuitry applies the indexed gains at the block 521 to thevectors which are summed. At a block 527, the decoder processingcircuitry modifies the gains to emphasize the contribution of vectorfrom the adaptive codebook 515. At a block 529, adaptive tiltcompensation is applied to the combined vectors with a goal offlattening the excitation spectrum. The decoder processing circuitryperforms synthesis filtering at the block 531 using the flattenedexcitation signal. Finally, to generate the reproduced speech signal539, post filtering is applied at a block 535 deemphasizing the valleyareas of the reproduced speech signal 539 to reduce the effect ofdistortion.

[0054] In the exemplary cellular telephony embodiment of the presentinvention, the A/D converter 115 (FIG. 1a) will generally involve analogto uniform digital PCM including: 1) an input level adjustment device;2) an input anti-aliasing filter; 3) a sample-hold device sampling at 8kHz; and 4) analog to uniform digital conversion to 13-bitrepresentation.

[0055] Similarly, the D/A converter 135 will generally involve uniformdigital PCM to analog including: 1) conversion from 13-bit/8 kHz uniformPCM to analog; 2) a hold device; 3) reconstruction filter includingx/sin(x) correction; and 4) an output level adjustment device.

[0056] In terminal equipment, the A/D function may be achieved by directconversion to 13-bit uniform PCM format, or by conversion to 8-bit/A-lawcompounded format. For the D/A operation, the inverse operations takeplace.

[0057] The encoder 117 receives data samples with a resolution of 13bits left justified in a 16-bit word. The three least significant bitsare set to zero. The decoder 133 outputs data in the same format.Outside the speech codec, further processing can be applied toaccommodate traffic data having a different representation.

[0058] A specific embodiment of an AMR (adaptive multi-rate) codec withthe operational functionality illustrated in FIGS. 2-5 uses five sourcecodecs with bit-rates 11.0, 8.0, 6.65, 5.8 and 4.55 kbps. Four of thehighest source coding bit-rates are used in the full rate channel andthe four lowest bit-rates in the half rate channel.

[0059] All five source codecs within the AMR codec are generally basedon a code-excited linear predictive (CELP) coding model. A 10th orderlinear prediction (LP), or short-term, synthesis filter, e.g., used atthe blocks 249, 267, 301, 407 and 531 (of FIGS. 2-5), is used which isgiven by: $\begin{matrix}{{{H(z)} = {\frac{1}{\hat{A}(z)} = \frac{1}{1 + {\sum\limits_{i = 1}^{m}{{\hat{a}}_{i}z^{- i}}}}}},} & (1)\end{matrix}$

[0060] where â_(i),i=1, . . . , m, are the (quantized) linear prediction(LP) parameters.

[0061] A long-term filter, i.e., the pitch synthesis filter, isimplemented using the either an adaptive codebook approach or a pitchpre-processing approach. The pitch synthesis filter is given by:$\begin{matrix}{{\frac{1}{B(z)} = \frac{1}{1 - {g_{p}z^{- T}}}},} & (2)\end{matrix}$

[0062] where T is the pitch delay and g_(p) is the pitch gain.

[0063] With reference to FIG. 2, the excitation signal at the input ofthe short-term LP synthesis filter at the block 249 is constructed byadding two excitation vectors from the adaptive and the fixed codebooks257 and 261, respectively. The speech is synthesized by feeding the twoproperly chosen vectors from these codebooks through the short-termsynthesis filter at the block 249 and 267, respectively.

[0064] The optimum excitation sequence in a codebook is chosen using ananalysis-by-synthesis search procedure in which the error between theoriginal and synthesized speech is minimized according to a perceptuallyweighted distortion measure. The perceptual weighting filter, e.g., atthe blocks 251 and 268, used in the analysis-by-synthesis searchtechnique is given by: $\begin{matrix}{{{W(z)} = \frac{A\left( {z/\gamma_{1}} \right)}{A\left( {z/\gamma_{2}} \right)}},} & (3)\end{matrix}$

[0065] where A(z) is the unquantized LP filter and 0<γ₂<γ₁≲1 are theperceptual weighting factors. The values γ₁=[0.9, 0.94] and γ₂=0.6 areused. The weighting filter, e.g., at the blocks 251 and 268, uses theunquantized LP parameters while the formant synthesis filter, e.g., atthe blocks 249 and 267, uses the quantized LP parameters. Both theunquantized and quantized LP parameters are generated at the block 239.

[0066] The present encoder embodiment operates on 20 ms (millisecond)speech frames corresponding to 160 samples at the sampling frequency of8000 samples per second. At each 160 speech samples, the speech signalis analyzed to extract the parameters of the CELP model, i.e., the LPfilter coefficients, adaptive and fixed codebook indices and gains.These parameters are encoded and transmitted. At the decoder, theseparameters are decoded and speech is synthesized by filtering thereconstructed excitation signal through the LP synthesis filter.

[0067] More specifically, LP analysis at the block 239 is performedtwice per frame but only a single set of LP parameters is converted toline spectrum frequencies (LSF) and vector quantized using predictivemulti-stage quantization (PMVQ). The speech frame is divided intosubframes. Parameters from the adaptive and fixed codebooks 257 and 261are transmitted every subframe. The quantized and unquantized LPparameters or their interpolated versions are used depending on thesubframe. An open-loop pitch lag is estimated at the block 241 once ortwice per frame for PP mode or LTP mode, respectively.

[0068] Each subframe, at least the following operations are repeated.First, the encoder processing circuitry (operating pursuant to softwareinstruction) computes x(n), the first target signal 229, by filteringthe LP residual through the weighted synthesis filter W(z)H(z) with theinitial states of the filters having been updated by filtering the errorbetween LP residual and excitation. This is equivalent to an alternateapproach of subtracting the zero input response of the weightedsynthesis filter from the weighted speech signal.

[0069] Second, the encoder processing circuitry computes the impulseresponse, h(n), of the weighted synthesis filter. Third, in the LTPmode, closed-loop pitch analysis is performed to find the pitch lag andgain, using the first target signal 229, x(n), and impulse response,h(n), by searching around the open-loop pitch lag. Fractional pitch withvarious sample resolutions are used.

[0070] In the PP mode, the input original signal has beenpitch-preprocessed to match the interpolated pitch contour, so noclosed-loop search is needed. The LTP excitation vector is computedusing the interpolated pitch contour and the past synthesizedexcitation.

[0071] Fourth, the encoder processing circuitry generates a new targetsignal X₂(n), the second target signal 253, by removing the adaptivecodebook contribution (filtered adaptive code vector) from x(n). Theencoder processing circuitry uses the second target signal 253 in thefixed codebook search to find the optimum innovation.

[0072] Fifth, for the 11.0 kbps bit rate mode, the gains of the adaptiveand fixed codebook are scalar quantized with 4 and 5 bits respectively(with moving average prediction applied to the fixed codebook gain). Forthe other modes the gains of the adaptive and fixed codebook are vectorquantized (with moving average prediction applied to the fixed codebookgain).

[0073] Finally, the filter memories are updated using the determinedexcitation signal for finding the first target signal in the nextsubframe.

[0074] The bit allocation of the AMR codec modes is shown in table 1.For example, for each 20 ms speech frame, 220, 160, 133, 116 or 91 bitsare produced, corresponding to bit rates of 11.0, 8.0, 6.65, 5.8 or 4.55kbps, respectively. TABLE 1 Bit allocation of the AMR coding algorithmfor 20 ms frame CODING RATE 11.0 KBPS 8.0 KBPS 6.65 KBPS 5.80 KBPS 4.55KBPS Frame size 20 ms Look ahead 5 ms LPC order 10^(th)-order Predictorfor LSF 1 predictor: 2 predictors Quantization 0 bit/frame 1 bit/frameLSF Quantization 28 bit/frame 24 bit/frame 18 LPC interpolation 2bits/frame 2 bits/f 0 2 bits/f 0 0 0 Coding mode bit 0 bit 0 bit 1bit/frame 0 bit 0 bit Pitch mode LTP LTP LTP PP PP PP Subframe size 5 msPitch Lag 30 bits/frame (9696) 8585 8585 0008 0008 0008 Fixed excitation31 bits/subframe 20 13 18 14 bits/subframe 10 bits/subframe Gainquantization 9 bits (scalar) 7 bits/subframe 6 bits/subframe Total 220bits/frame 160 133 133 116 91

[0075] With reference to FIG. 5, the decoder processing circuitry,pursuant to software control, reconstructs the speech signal using thetransmitted modeling indices extracted from the received bit stream bythe demultiplexor 511. The decoder processing circuitry decodes theindices to obtain the coder parameters at each transmission frame. Theseparameters are the LSF vectors, the fractional pitch lags, theinnovative code vectors, and the two gains.

[0076] The LSF vectors are converted to the LP filter coefficients andinterpolated to obtain LP filters at each subframe. At each subframe,the decoder processing circuitry constructs the excitation signal by: 1)identifying the adaptive and innovative code vectors from the codebooks515 and 519; 2) scaling the contributions by their respective gains atthe block 521; 3) summing the scaled contributions; and 3) modifying andapplying adaptive tilt compensation at the blocks 527 and 529. Thespeech signal is also reconstructed on a subframe basis by filtering theexcitation through the LP synthesis at the block 531. Finally, thespeech signal is passed through an adaptive post filter at the block 535to generate the reproduced speech signal 539.

[0077] The AMR encoder will produce the speech modeling information in aunique sequence and format, and the AMR decoder receives the sameinformation in the same way. The different parameters of the encodedspeech and their individual bits have unequal importance with respect tosubjective quality. Before being submitted to the channel encodingfunction the bits are rearranged in the sequence of importance.

[0078] Two pre-processing functions are applied prior to the encodingprocess: high-pass filtering and signal down-scaling. Down-scalingconsists of dividing the input by a factor of 2 to reduce thepossibility of overflows in the fixed point implementation. Thehigh-pass filtering at the block 215 (FIG. 2) serves as a precautionagainst undesired low frequency components. A filter with cut offfrequency of 80 Hz is used, and it is given by:${H_{hl}(z)} = \frac{0.92727435 - {1.8544941z^{- 1}} + {0.92727435\quad z^{- 2}}}{1 - {1.9059465\quad z^{- 1}} + {0.9114024\quad z^{- 2}}}$

[0079] Down scaling and high-pass filtering are combined by dividing thecoefficients of the numerator of H_(hl)(z) by2.

[0080] Short-term prediction, or linear prediction (LP) analysis isperformed twice per speech frame using the autocorrelation approach with30 ms windows. Specifically, two LP analyses are performed twice perframe using two different windows. In the first LP analysis(LP_analysis_(—)1), a hybrid window is used which has its weightconcentrated at the fourth subframe. The hybrid window consists of twoparts. The first part is half a Hamming window, and the second part is aquarter of a cosine cycle. The window is given by:${w_{1}(n)} = \left\{ \begin{matrix}{\quad {{0.54 - {0.46{\cos \left( \frac{\pi \quad n}{L} \right)}}},}} & {\quad {{n = {0\quad {to}\quad 214}},{L = 215}}} \\{\quad {{\cos \left( \frac{0.49\left( {n - L} \right)\pi}{25} \right)},}} & {\quad {n = {215\quad {to}\quad 239}}}\end{matrix} \right.$

[0081] In the second LP analysis (LP—analysis—2), a symmetric Hammingwindow is used. ${w_{2}(n)} = \left\{ \begin{matrix}{0.54 - {0.46\quad {\cos \left( \frac{\pi \quad n}{L} \right)}}} & {{n = {0\quad {to}\quad 119}},{L = 120}} \\{{0.54 + {0.46{\cos \left( \frac{\left( {n - L} \right)\pi}{120} \right)}}},} & {n = {120\quad {to}\quad 239}}\end{matrix} \right.$

[0082] In either LP analysis, the autocorrelations of the windowedspeech s (n),n=0,239 are computed by:${{r(k)} = {\sum\limits_{n = k}^{239}{{\overset{.}{s}(n)}{\overset{.}{s}\left( {n - k} \right)}}}},{k = {0,10.}}$

[0083] A 60 Hz bandwidth expansion is used by lag windowing, theautocorrelations using the window:${{w_{lag}(i)} = {\exp \left\lbrack {{- \frac{1}{2}}\left( \frac{2\pi \quad 60i}{8000} \right)^{2}} \right\rbrack}},{i = {1,10.}}$

[0084] Moreover, r(0) is multiplied by a white noise correction factor1.0001 which is equivalent to adding a noise floor at −40 dB.

[0085] The modified autocorrelations r (0)=1.0001r(0) and r(k)=r(k)w_(lag) (k), k=1,10 are used to obtain the reflectioncoefficients k_(i) and LP filter coefficients a_(i), i=1,10 using theLevinson-Durbin algorithm. Furthermore, the LP filter coefficients a_(i)are used to obtain the Line Spectral Frequencies (LSFs).

[0086] The interpolated unquantized LP parameters are obtained byinterpolating the LSF coefficients obtained from the LP analysis_(—)1and those from LP_analysis_(—)2 as:

q ₁(n)=0.5q ₄(n−1)+0.5q ₂(n)

q ₃(n)=0.5q ₂(n)+0.5q ₄(n)

[0087] where q₁(n) is the interpolated LSF for subframe 1, q₂(n) is theLSF of subframe 2 obtained from LP_analysis_(—)2 of current frame, q₃(n)is the interpolated LSF for subframe 3, q₄(n−1) is the LSF (cosinedomain) from LP_analysis_(—)1 of previous frame, and q₄(n) is the LSFfor subframe 4 obtained from LP_analysis_(—)1 of current frame. Theinterpolation is carried out in the cosine domain.

[0088] A VAD (Voice Activity Detection) algorithm is used to classifyinput speech frames into either active voice or inactive voice frame(background noise or silence) at a block 235 (FIG. 2).

[0089] The input speech s(n) is used to obtain a weighted speech signals_(w)(n) by passing s(n) through a filter:${W(z)} = {\frac{A\left( \frac{z}{\gamma 1} \right)}{A\left( \frac{z}{\gamma 2} \right)}.}$

[0090] That is, in a subframe of size L_SF, the weighted speech is givenby:${{s_{w}(n)} = {{s(n)} + {\sum\limits_{i = 1}^{10}{a_{i}\gamma_{1}^{i}{s\left( {n - i} \right)}}} - {\sum\limits_{i = 1}^{10}{a_{i}\gamma_{2}^{i}{s_{w}\left( {n - i} \right)}}}}},{n = 0},{{L\_ SF} - 1.}$

[0091] A voiced/unvoiced classification and mode decision within theblock 279 using the input speech s(n) and the residual r_(w)(n) isderived where:${{r_{w}(n)} = {{s(n)} + {\sum\limits_{i = 1}^{10}{a_{i}\gamma_{1}^{i}{s\left( {n - i} \right)}}}}},{n = 0},{{L\_ SF} - 1.}$

[0092] The classification is based on four measures: 1) speech sharpnessP1_SHP; 2) normalized one delay correlation P2_R1; 3) normalizedzero-crossing rate P3_ZC; and 4) normalized LP residual energy P4_RE.

[0093] The speech sharpness is given by:${{P1\_ SHP} = \frac{\sum\limits_{n = 0}^{L}{{abs}\left( {r_{w}(n)} \right)}}{{Max}\quad L}},$

[0094] where Max is the maximum of abs(r_(w)(n)) over the specifiedinterval of length L. The normalized one delay correlation andnormalized zero-crossing rate are given by:${P2\_ R1} = \frac{\sum\limits_{n = 0}^{L - 1}{{s(n)}{s\left( {n + 1} \right)}}}{\sqrt{\sum\limits_{n = 0}^{L - 1}{{s(n)}{s(n)}{\sum\limits_{n = 0}^{L - 1}{{s\left( {n + 1} \right)}{s\left( {n + 1} \right)}}}}}}$${{P3\_ ZC} = {\frac{1}{2L}{\sum\limits_{i = 0}^{L - 1}\left\lbrack \left| {{{sgn}\left\lbrack {s(i)} \right\rbrack} - {{sgn}\left\lbrack {s\left( {i - 1} \right)} \right\rbrack}} \right| \right\rbrack}}},$

[0095] where sgn is the sign function whose output is either 1 or −1depending that the input sample is positive or negative. Finally, thenormalized LP residual energy is given by:

P4_RE=1−{square root}{square root over (lpc_gain)}

[0096] where${{lpc\_ gain} = {\prod\limits_{i = 1}^{10}\left( {1 - k_{i}^{2}} \right)}},$

[0097] where k_(i) are the reflection coefficients obtained from LPanalysis_(—)1.

[0098] The voiced/unvoiced decision is derived if the followingconditions are met:

if P2_(—) R1<0.6 and P1_SHP>0.2 set mode=2,

if P3_ZC>0.4 and P1_SHP>0.18 set mode=2,

if P4_RE<0.4 and P1_SHP>0.2 set mode=2,

if (P2_(—) R1<−1.2+3.2P1_SHP) set VUV=−3

if (P4_RE<−0.21+1.4286P1_SHP) set VUV=−3

if (P3_ZC>0.8−0.6P1_SHP) set VUV=−3

if (P4_RE<0.1) set VUV=−3

[0099] Open loop pitch analysis is performed once or twice (each 10 ms)per frame depending on the coding rate in order to find estimates of thepitch lag at the block 241 (FIG. 2). It is based on the weighted speechsignal s_(w)(n+n_(m)),n=0,1 , . . . , 79, in which n_(m) defines thelocation of this signal on the first half frame or the last half frame.In the first step, four maxima of the correlation:$C_{k} = {\sum\limits_{n = 0}^{79}{{s_{w}\left( {n_{m} + n} \right)}{s_{w}\left( {n_{m} + n - k} \right)}}}$

[0100] are found in the four ranges 17 . . . 33, 34 . . . 67, 68 . . .135, 136 . . . 145, respectively. The retained maxima C_(k) _(i) ,i=1,2,3,4, are normalized by dividing by:

{square root}{square root over (Σ_(n) s _(w) ²(n _(m) +n−k),)}i=1 , . .. , 4, respectively.

[0101] The normalized maxima and corresponding delays are denoted by(R_(i),k_(i)),i=1,2,3,4.

[0102] In the second step, a delay, k_(I), among the four candidates, isselected by maximizing the four normalized correlations. In the thirdstep, k_(I) is probably corrected to k_(i)(i<I) by favoring the lowerranges. That is, k_(i)(i<I) is selected if k_(i) is within [k_(I)/m−4,k_(I)/m+4],m=2,3,4,5, and if k_(i)>k_(I) 0.95^(I−i)D, i<I, where D is1.0, 0.85, or 0.65, depending on whether the previous frame is unvoiced,the previous frame is voiced and k_(i) is in the neighborhood (specifiedby ±8) of the previous pitch lag, or the previous two frames are voicedand k_(i) is in the neighborhood of the previous two pitch lags. Thefinal selected pitch lag is denoted by T_(op).

[0103] A decision is made every frame to either operate the LTP(long-term prediction) as the traditional CELP approach (LTP_mode=1), oras a modified time warping approach (LTP_mode=0) herein referred to asPP (pitch preprocessing). For 4.55 and 5.8 kbps encoding bit rates,LTP_mode is set to 0 at all times. For 8.0 and 11.0 kbps, LTP_mode isset to 1 all of the time. Whereas, for a 6.65 kbps encoding bit rate,the encoder decides whether to operate in the LTP or PP mode. During thePP mode, only one pitch lag is transmitted per coding frame.

[0104] For 6.65 kbps, the decision algorithm is as follows. First, atthe block 241, a prediction of the pitch lag pit for the current frameis determined as follows:

[0105] if (LTP_MODE_m═1)

[0106] pit=lagl1+2.4*(lag_f[3]−lagl1);

[0107] else

[0108] pit=lag_f[1]+2.75*(lag_f[3]−lag_f[1]);

[0109] where LTP_mode_m is previous frame LTP_mode, lag_f[1],lag_f[3]are the past closed loop pitch lags for second and fourth subframesrespectively, lagl is the current frame open-loop pitch lag at thesecond half of the frame, and, lagl1 is the previous frame open-looppitch lag at the first half of the frame.

[0110] Second, a normalized spectrum difference between the LineSpectrum Frequencies (LSF) of current and previous frame is computed as:${{e\_ lsf} = {\frac{1}{10}{\sum\limits_{i = 0}^{9}{{abs}\left( {{{LSF}(i)} - {{LSF\_ m}\quad (i)}} \right)}}}},$

[0111] if (abs(pit−lagl)<TH and abs(lag_f[3]−lagl)<lagl*0.2)

[0112] if (Rp>0.5 && pgain_past>0.7 and e_lsf<0.5/30) LTP_mode=0;

[0113] else LTP_mode=1;

[0114] where Rp is current frame normalized pitch correlation,pgain_past is the quantized pitch gain from the fourth subframe of thepast frame, TH=MIN(lagl*0.1, 5), and TH=MAX(2.0, TH).

[0115] The estimation of the precise pitch lag at the end of the frameis based on the normalized correlation:${R_{k} = \frac{\sum\limits_{n = 0}^{L}{{s_{w}\left( {n + {nl}} \right)}{s_{w}\left( {n + {nl} - k} \right)}}}{\sqrt{\sum\limits_{n = 0}^{L}{s_{w}^{2}\left( {n + {nl} - k} \right)}}}},$

[0116] where s_(w)(n+n1), n=0,1 . . . , L−1, represents the last segmentof the weighted speech signal including the look-ahead ( the look-aheadlength is 25 samples), and the size L is defined according to theopen-loop pitch lag T_(op) with the corresponding normalized correlationC_(T) _(op) :

[0117] if(C_(T) _(op) >0.6)

[0118] L=max{50, T_(op)}

[0119] L=min{80, L}

[0120] else

[0121] L=80

[0122] In the first step, one integer lag k is selected maximizing theR_(k) in the range kε[T_(op)−10, T_(op)+10] bounded by [17, 145]. Then,the precise pitch lag P_(m) and the corresponding index I_(m) for thecurrent frame is searched around the integer lag, [k−1, k+1], byup-sampling R_(k).

[0123] The possible candidates of the precise pitch lag are obtainedfrom the table named as PitLagTab8b[i], i=0,1 , . . . , 127. In the laststep, the precise pitch lag P_(m)=PitLagTab8b[I_(m)] is possiblymodified by checking the accumulated delay τ_(acc) due to themodification of the speech signal:

[0124] if(τ_(acc)>5) I_(m)<═min{I_(m)+1, 127}, and

[0125] if(τ_(acc)<−5) I_(m)<═max{I_(m)−1,0}.

[0126] The precise pitch lag could be modified again:

[0127] if(τ_(acc)>10) I_(m)<═min{I_(m)+1, 127}, and

[0128] if(τ_(aac)<−10) I_(m)<═max{I_(m)−1,0}.

[0129] The obtained index I_(m) will be sent to the decoder.

[0130] The pitch lag contour, τ_(c)(n), is defined using both thecurrent lag P_(m) and the previous lag P_(m−l):

[0131] if ( |P _(m) −P _(m−1)|<0.2 min {P _(m) , P _(m−1)})

[0132] τ_(c)(n)=P_(m−1)+n(P_(m)−P_(m−1))/L_(f), n=0,1 , . . . , L_(f)−1

[0133] τ_(c)(n)=P_(m), n=L_(f), . . . , 170

[0134] else

[0135] τ_(c)(n)=P_(m−1), n=0,1 , . . . , 39;

[0136] τ_(c)(n)=P_(m), n=40 , . . . , 170

[0137] where L_(f)=160 is the frame size.

[0138] One frame is divided into 3 subframes for the long-termpreprocessing. For the first two subframes, the subframe size, L_(s), is53, and the subframe size for searching, L_(sr), is 70. For the lastsubframe, L_(s) is 54 and L_(sr), is:

L _(sr)=min{70, L _(s) +L _(khd)−10−τ_(acc)},

[0139] where L_(khd)=25 is the look-ahead and the maximum of theaccumulated delay τ_(acc) is limited to 14.

[0140] The target for the modification process of the weighted speechtemporally memorized in {ŝ_(w)(m0+n), n=0,1 , . . . , L_(sr) −1} iscalculated by warping the past modified weighted speech buffer, ŝ_(w)(m0+n), n<0, with the pitch lag contour, τ_(c)(n+m·L_(s)), m=0,1,2,${{{\hat{s}}_{w}\left( {{m0} + n} \right)} = {\sum\limits_{i = {- f_{1}}}^{f_{1}}{{{\hat{s}}_{w}\left( {{m0} + n - {T_{c}(n)} + i} \right)}{I_{s}\left( {i,{T_{IC}(n)}} \right)}}}},{n = 0},1,\ldots \quad,{L_{sr} - 1},$

[0141] where T_(C)(n) and T_(IC)(N) are calculated by:

T _(c)(n)=trunc{τ_(c)(n+m·L _(s))},

T _(IC)(n)=τ_(c)(n)−T_(C)(n),

[0142] m is subframe number, I_(s)(i,T_(IC)(n)) is a set ofinterpolation coefficients, and f_(l) is 10. Then, the target formatching, ŝ_(t)(n), n=0,1 , . . . , L_(sr)−1, is calculated by weightingŝ_(w)(m0+n), n=0,1 , . . . , L_(sr)−1, in the time domain:

ŝ _(t)(n)=n·ŝ _(w)(m0+n)/L _(s) , n=0,1, . . . , L _(s)−1,

ŝ _(t)(n)=ŝ _(w)(m0+n),n=L _(s) ,, . . . , L _(sr)−1

[0143] The local integer shifting range [SR0, SR1] for searching for thebest local delay is computed as the following:

[0144] if speech is unvoiced

[0145] SR0=−1,

[0146] SR1=1,

[0147] else

[0148] SR0=round{−4 min{1.0, max{0.0, 1−0.4 (P_(sh)−0.2)}}}

[0149] SR1=round{4 min{1.0, max{0.0, 1−0.4 (P_(sh)−0.2)}}}

[0150] where P_(sh)=max{P_(sh1), P_(sh2)}, P_(sh1) is the average topeak ratio (i.e., sharpness) from the target signal:$P_{sh1} = \frac{\sum\limits_{n = 0}^{L_{sr} - 1}\left| {{\hat{s}}_{w}\left( {{m0} + n} \right)} \right|}{L_{sr}\max \left\{ {\left| {{\hat{s}}_{w}\left( {{m0} + n} \right)} \right|,{n = 0},1,\ldots \quad,{L_{sr} - 1}} \right\}}$

[0151] and P_(sh2) is the sharpness from the weighted speech signal:$P_{sh2} = \frac{\sum\limits_{n = 0}^{L_{sr} - {L_{s}/2} - 1}\left| {s_{w}\left( {n + {n0} + {L_{s}/2}} \right)} \right|}{\begin{matrix}{\left( {L_{sr} - {L_{s}/2}} \right)\max \left\{ {\left| {s_{w}\left( {n + {n0} + {L_{s}/2}} \right)} \right|,} \right.} \\\left. {{n = 0},1,\ldots \quad,{L_{sr} - {L_{s}/2} - 1}} \right\}\end{matrix}}$

[0152] where n0=trunc{m0+τ_(acc)+0.5} (here, m is subframe number andτ_(acc) is the previous accumulated delay).

[0153] In order to find the best local delay, τ_(opt), at the end of thecurrent processing subframe, a normalized correlation vector between theoriginal weighted speech signal and the modified matching target isdefined as:${R_{I}(k)} = \frac{\sum\limits_{n = 0}^{L_{sr} - 1}{{s_{w}\left( {{n0} + n + k} \right)}{{\hat{s}}_{t}(n)}}}{\sqrt{\sum\limits_{n = 0}^{L_{sr} - 1}{{s_{w}^{2}\left( {{n0} + n + k} \right)}{\sum\limits_{n = 0}^{L_{sr} - 1}{{\hat{s}}_{t}^{2}(n)}}}}}$

[0154] A best local delay in the integer domain, k_(opt), is selected bymaximizing R_(I)(k) in the range of kε[SR0, SR1] , which iscorresponding to the real delay:

k _(r) =k _(opt) +n0−m0−τ_(acc)

[0155] If R_(I)(k_(opt))<0.5, k_(r) is set to zero.

[0156] In order to get a more precise local delay in the range{k_(r)−0.75+0.1j, j=0,1 , . . . 15} around k_(r), R_(I)(k) isinterpolated to obtain the fractional correlation vector, R_(f)(j), by:${{R_{f}(j)} = {\sum\limits_{i = {- 7}}^{8}{{R_{I}\left( {k_{opt} + I_{j} + i} \right)}{I_{f}\left( {i,j} \right)}}}},\quad {j = 0},1,\ldots \quad,15,$

[0157] where {I_(f)(i,j)} is a set of interpolation coefficients. Theoptimal fractional delay index, j_(opt), is selected by maximizingR_(f)(j). Finally, the best local delay, τ_(opt), at the end of thecurrent processing subframe, is given by,

τ_(opt) =k _(r)−0.75+0.1j _(opt)

[0158] The local delay is then adjusted by:$\tau_{opt} = \left\{ \begin{matrix}{0,} & {{{{if}\quad \tau_{acc}} + \tau_{opt}} > 14} \\{\tau_{opt},} & {otherwise}\end{matrix} \right.$

[0159] The modified weighted speech of the current subframe, memorizedin {ŝ_(w)(m0+n), n=0,1 , . . . , L_(s) −1} to update the buffer andproduce the second target signal 253 for searching the fixed codebook261, is generated by warping the original weighted speech {s _(w)(n)}from the original time region,

[m0+τ_(acc) , m0+τ_(acc) +L _(s)+τ_(opt)],

[0160] to the modified time region,

[m0, m0+L_(s)]:

[0161]${{{\hat{s}}_{w}\left( {{m0} + n} \right)} = {\sum\limits_{i = {{- f_{1}} + 1}}^{f_{1}}{{s_{w}\left( {{m0} + n + {T_{W}(n)} + i} \right)}{I_{s}\left( {i,{T_{IW}(n)}} \right)}}}},{n = 0},1,\ldots \quad,{L_{s} - 1},$

[0162] where T_(W)(n) and T_(IW)(n) are calculated by:

T _(W)(n)=trunc{τ_(acc) +n·τ _(opt) /L _(s},)

T _(IW)(n)=τ_(acc+) n·τ _(opt) /L _(s) −T _(W)(n),

[0163] {I_(s)(i, T_(IW)(n))} is a set of interpolation coefficients.

[0164] After having completed the modification of the weighted speechfor the current subframe, the modified target weighted speech buffer isupdated as follows:

ŝ _(w)(n)<═ŝ _(w)(n+L _(s)), n=0,1 , . . . , n _(m)−1.

[0165] The accumulated delay at the end of the current subframe isrenewed by:

τ_(acc)<═τ_(acc)+τ_(opt).

[0166] Prior to quantization the LSFs are smoothed in order to improvethe perceptual quality. In principle, no smoothing is applied duringspeech and segments with rapid variations in the spectral envelope.During non-speech with slow variations in the spectral envelope,smoothing is applied to reduce unwanted spectral variations. Unwantedspectral variations could typically occur due to the estimation of theLPC parameters and LSF quantization. As an example, in stationarynoise-like signals with constant spectral envelope introducing even verysmall variations in the spectral envelope is picked up easily by thehuman ear and perceived as an annoying modulation.

[0167] The smoothing of the LSFs is done as a running mean according to:

lsf _(i)(n)=β(n)·lsf _(i)(n−1)+(1−β(n))·lsf _(—) est _(i)(n), i=1 , . .. , 10

[0168] where lsf_est_(i)(n) is the i^(th) estimated LSF of frame n, andlsf_(i)(n) is the i^(th) LSF for quantization of frame n. The parameterβ(n) controls the amount of smoothing, e.g. if β(n) is zero no smoothingis applied.

[0169] β(n) is calculated from the VAD information (generated at theblock 235) and two estimates of the evolution of the spectral envelope.The two estimates of the evolution are defined as:${\Delta \quad {SP}} = {\sum\limits_{i = 1}^{10}\left( {{{lsf\_ est}_{i}(n)} - {{lsf\_ est}_{i}\left( {n - 1} \right)}} \right)^{2}}$${\Delta \quad {SP}_{int}} = {\sum\limits_{i = 1}^{10}\left( {{{lsf\_ est}_{i}(n)} - {{ma\_ lsf}_{i}\left( {n - 1} \right)}} \right)^{2}}$

ma _(—) lsf _(i)(n)=β(n)·ma _(—) lsf _(i)(n−1)+(1−β(n))·lsf _(—) est_(i)(n), i=1 , . . . , 10

[0170] The parameter β(n) is controlled by the following logic:

[0171] Step 1:

[0172] if (Vad=1|PastVad=1|k₁>0.5)

[0173] N_(mode) _(—) _(frm)(n−1)=0

[0174] β(n)=0.0

[0175] elseif (N_(mode) _(—)_(frm)(n−1)>0&(ΔSP>0.0015|ΔSP_(int)>0.0024))

[0176] N_(mode) _(—) _(frm)(n−1)=0

[0177] β(n)=0.0

[0178] elseif (N_(mode) _(—) _(frm)(n−1)>1&ΔSP>0.0025)

[0179] N_(mode) _(—) ^(frm)(n−1)=1

[0180] endif

[0181] Step 2: if(Vad = 0&PastVad = 0)N_(mode_frm)(n) = N_(mode_frm)(n − 1) + 1 if(N_(mode_frm)(n) > 5)N_(mode_frm)(n) = 5 endif${\beta (n)} = {\frac{0.9}{16} \cdot \left( {{N_{mode\_ frm}(n)} - 1} \right)^{2}}$else N_(mode_frm)(n) = N_(mode_frm)(n − 1) endif

[0182] where k₁ is the first reflection coefficient.

[0183] In step 1, the encoder processing circuitry checks the VAD andthe evolution of the spectral envelope, and performs a full or partialreset of the smoothing if required. In step 2, the encoder processingcircuitry updates the counter, N_(mode) _(—) _(frm)(n), and calculatesthe smoothing parameter, β(n). The parameter β(n) varies between 0.0 and0.9, being 0.0 for speech, music, tonal-like signals, and non-stationarybackground noise and ramping up towards 0.9 when stationary backgroundnoise occurs.

[0184] The LSFs are quantized once per 20 ms frame using a predictivemulti-stage vector quantization. A minimal spacing of 50 Hz is ensuredbetween each two neighboring LSFs before quantization. A set of weightsis calculated from the LSFs, given by w_(i)=K|P(f_(i))|⁰ ⁴ where f_(i)is the i^(th) LSF value and P(f_(i)) is the LPC power spectrum at f_(i)(K is an irrelevant multiplicative constant). The reciprocal of thepower spectrum is obtained by (up to a multiplicative constant):${P\left( f_{i} \right)}^{- 1} \sim \left\{ \begin{matrix}\left( {1 - {{\cos \left( {2\pi \quad f_{i}} \right)}{\prod\limits_{{odd}\quad j}\left\lbrack {{\cos \left( {2\pi \quad f_{i}} \right)} - {\cos \left( {2\pi \quad f_{j}} \right)}} \right\rbrack^{2}}}} \right. & {{even}\quad i} \\\left( {1 + {{\cos \left( {2\pi \quad f_{i}} \right)}{\prod\limits_{{even}\quad j}\left\lbrack {{\cos \left( {2\pi \quad f_{i}} \right)} - {\cos \left( {2\pi \quad f_{j}} \right)}} \right\rbrack^{2}}}} \right. & {{odd}\quad i}\end{matrix} \right.$

[0185] and the power of −0.4 is then calculated using a lookup table andcubic-spline interpolation between table entries.

[0186] A vector of mean values is subtracted from the LSFs, and a vectorof prediction error vector fe is calculated from the mean removed LSFsvector, using a full-matrix AR(2) predictor. A single predictor is usedfor the rates 5.8, 6.65, 8.0, and 11.0 kbps coders, and two sets ofprediction coefficients are tested as possible predictors for the 4.55kbps coder.

[0187] The vector of prediction error is quantized using a multi-stageVQ, with multi-surviving candidates from each stage to the next stage.The two possible sets of prediction error vectors generated for the 4.55kbps coder are considered as surviving candidates for the first stage.

[0188] The first 4 stages have 64 entries each, and the fifth and lasttable have 16 entries. The first 3 stages are used for the 4.55 kbpscoder, the first 4 stages are used for the 5.8, 6.65 and 8.0 kbpscoders, and all 5 stages are used for the 11.0 kbps coder. The followingtable summarizes the number of bits used for the quantization of theLSFs for each rate. 1^(st) 2^(nd) 3^(rd) 4^(th) 5^(th) prediction stagestage stage stage stage total 4.55 kbps 1 6 6 6 19  5.8 kbps 0 6 6 6 624 6.65 kbps 0 6 6 6 6 24  8.0 kbps 0 6 6 6 6 24 11.0 kbps 0 6 6 6 6 428

[0189] The number of surviving candidates for each stage is summarizedin the following table. prediction Surviving surviving survivingsurviving candidates candidates candidates candidates candidates intothe 1^(st) from the from the from the from the stage 1^(st) stage 2^(nd)stage 3^(rd) stage 4^(th) stage  4.55 kbps 2 10  6 4 5.8 kbps 1 8 6 4 6.65 kbps 1 8 8 4 8.0 kbps 1 8 8 4 11.0 kbps 1 8 6 4 4

[0190] The quantization in each stage is done by minimizing the weighteddistortion measure given by:$ɛ_{k} = {\sum\limits_{i = 0}^{9}{\left( {w_{i}\left( {{fe}_{i} - C_{i}^{k}} \right)} \right)^{2}.}}$

[0191] The code vector with index k_(min) which minimizes ε_(k) suchthat ε_(k) _(min) <ε_(k) for all k , is chosen to represent theprediction/quantization error (fe represents in this equation both theinitial prediction error to the first stage and the successivequantization error from each stage to the next one).

[0192] The final choice of vectors from all of the surviving candidates(and for the 4.55 kbps coder—also the predictor) is done at the end,after the last stage is searched, by choosing a combined set of vectors(and predictor) which minimizes the total error. The contribution fromall of the stages is summed to form the quantized prediction errorvector, and the quantized prediction error is added to the predictionstates and the mean LSFs value to generate the quantized LSFs vector.

[0193] For the 4.55 kbps coder, the number of order flips of the LSFs asthe result of the quantization if counted, and if the number of flips ismore than 1, the LSFs vector is replaced with 0.9·(LSFs of previousframe)+0.1·(mean LSFs value). For all the rates, the quantized LSFs areordered and spaced with a minimal spacing of 50 Hz.

[0194] The interpolation of the quantized LSF is performed in the cosinedomain in two ways depending on the LTP_mode. If the LTP_mode is 0, alinear interpolation between the quantized LSF set of the current frameand the quantized LSF set of the previous frame is performed to get theLSF set for the first, second and third subframes as:

{overscore (q)} ₁(n)=0.75{overscore (q)} ₄(n−1)+0.25{overscore (q)} ₄(n)

{overscore (q)} ₂(n)=0.5{overscore (q)} ₄(n−1)+0.5{overscore (q)} ₄(n)

{overscore (q)} ₃(n)=0.25{overscore (q)} ₄(n−1)+0.75{overscore (q)} ₄(n)

[0195] where {overscore (q)}₄(n−1) and {overscore (q)}₄(n) are thecosines of the quantized LSF sets of the previous and current frames,respectively, and {overscore (q)}₁(n), {overscore (q)}₂(n) and{overscore (q)}₃(n) are the interpolated LSF sets in cosine domain forthe first, second and third subframes respectively.

[0196] If the LTP_mode is 1, a search of the best interpolation path isperformed in order to get the interpolated LSF sets. The search is basedon a weighted mean absolute difference between a reference LSF setr{overscore (l)}(n) and the LSF set obtained from LP analysis_(—)2{overscore (l)}(n). The weights {overscore (w)}are computed as follows:

w(0)=(1−l(0))(1−l(1)+l(0))

w(9)=(1−l(9))(1−l(9)+l(8))

[0197] for i=1 to 9

w(i)=(1−l(i))(1−Min(l(i+1)−l(i),l(i)−l(i−1)))

[0198] where Min(a,b) returns the smallest of a and b.

[0199] There are four different interpolation paths. For each path, areference LSF set r{overscore (q)}(n) in cosine domain is obtained asfollows:

r{overscore (q)}(n)=α(k){overscore (q)} ₄(n)+(1−α(k)){overscore (q)}₄(n−1), k=1 to 4

[0200] {overscore (α)}={0.4,0.5,0.6, 0.7 } for each path respectively.Then the following distance measure is computed for each path as:

D=|r{overscore (l)}(n)−{overscore (l)}(n)|^(T) {overscore (w)}

[0201] The path leading to the minimum distance D is chosen and thecorresponding reference LSF set r{overscore (q)}(n) is obtained as:

r{overscore (q)}(n)=α_(opt) {overscore (q)} ₄(n)+(1−α_(opt)){overscore(q)} ₄(n− 1)

[0202] The interpolated LSF sets in the cosine domain are then given by:

{overscore (q)} ₁(n)=0.5{overscore (q)} ₄(n−1)+0.5r{overscore (q)}(n)

{overscore (q)} ₂(n)=r{overscore (q)}(n)

{overscore (q)} ₃(n)=0.5r{overscore (q)}(n)+0.5{overscore (q)} ₄(n)

[0203] The impulse response, h(n), of the weighted synthesis filterH(z)W(z)=A(z/γ₁)/[{overscore (A)}(z)A(z/γ₂)] is computed each subframe.This impulse response is needed for the search of adaptive and fixedcodebooks 257 and 261. The impulse response h(n) is computed byfiltering the vector of coefficients of the filter A(z/γ₁) extended byzeros through the two filters 1/{overscore (A)}(z) and 1/A(z/γ₂). Thetarget signal for the search of the adaptive codebook 257 is usuallycomputed by subtracting the zero input response of the weightedsynthesis filter H(z)W(z) from the weighted speech signal s_(w)(n). Thisoperation is performed on a frame basis. An equivalent procedure forcomputing the target signal is the filtering of the LP residual signalr(n) through the combination of the synthesis filter 1/{overscore(A)}(z) and the weighting filter W(z).

[0204] After determining the excitation for the subframe, the initialstates of these filters are updated by filtering the difference betweenthe LP residual and the excitation. The LP residual is given by:${{r(n)} = {{s(n)} + {\sum\limits_{i = 1}^{10}{{\overset{\_}{a}}_{i}{s\left( {n - i} \right)}}}}},{n = 0},{{L\_ SF} - 1}$

[0205] The residual signal r(n) which is needed for finding the targetvector is also used in the adaptive codebook search to extend the pastexcitation buffer. This simplifies the adaptive codebook searchprocedure for delays less than the subframe size of 40 samples.

[0206] In the present embodiment, there are two ways to produce an LTPcontribution. One uses pitch preprocessing (PP) when the PP-mode isselected, and another is computed like the traditional LTP when theLTP-mode is chosen. With the PP-mode, there is no need to do theadaptive codebook search, and LTP excitation is directly computedaccording to past synthesized excitation because the interpolated pitchcontour is set for each frame. When the AMR coder operates withLTP-mode, the pitch lag is constant within one subframe, and searchedand coded on a subframe basis.

[0207] Suppose the past synthesized excitation is memorized in {ext(MAX_LAG+n), n<0}, which is also called adaptive codebook. The LTPexcitation codevector, temporally memorized in { ext(MAX_LAG+n),0<=n<L_SF}, is calculated by interpolating the past excitation (adaptivecodebook) with the pitch lag contour, τ_(c)(n+m·L_SF), m=0,1,2,3. Theinterpolation is performed using an FIR filter (Hamming windowed sincfunctions):${{{ext}\left( {{{MA}\quad \overset{\rightharpoonup}{X}{\_ LAG}} + n} \right)} = {\sum\limits_{i = {- f_{i}}}^{f_{i}}{{{ext}\left( {{MAX\_ LAG} + n - {T_{c}(n)} + i} \right)} \cdot {I_{s}\left( {i,{T_{IC}(n)}} \right)}}}},{n = 0},1,\ldots \quad,{{L\_ SF} - 1},$

[0208] where T_(C)(n) and T_(IC)(n) are calculated by

T _(c)(n)=trunc{τ_(c)(n+m·L _(—) SF)},

T _(IC)(n)=τ_(c)(n)−T _(C)(n),

[0209] m is subframe number, {I_(s)(i,T_(IC)(n))} is a set ofinterpolation coefficients, f_(l) is 10, MAX_(—LAG is) 145+11, andL_SF=40 is the subframe size. Note that the interpolated values{ext(MAX_LAG+n), 0<=n<L_SF−17+11} might be used again to do theinterpolation when the pitch lag is small. Once the interpolation isfinished, the adaptive codevector Va={v_(a)(n),n=0 to 39} is obtained bycopying the interpolated values:

v _(a)(n)=ext(MAX_LAG+n), 0<=n<L _(—) SF

[0210] Adaptive codebook searching is performed on a subframe basis. Itconsists of performing closed-loop pitch lag search, and then computingthe adaptive code vector by interpolating the past excitation at theselected fractional pitch lag. The LTP parameters (or the adaptivecodebook parameters) are the pitch lag (or the delay) and gain of thepitch filter. In the search stage, the excitation is extended by the LPresidual to simplify the closed-loop search.

[0211] For the bit rate of 11.0 kbps, the pitch delay is encoded with 9bits for the 1^(St) and 3^(rd) subframes and the relative delay of theother subframes is encoded with 6 bits. A fractional pitch delay is usedin the first and third subframes with resolutions: ⅙in the range$\left\lbrack {17,{93\frac{4}{6}}} \right\rbrack,$

[0212] and integers only in the range [95,145]. For the second andfourth subframes, a pitch resolution of ⅙is always used for the rate11.0 kbps in the range$\left\lbrack {{T_{1} - {5\frac{3}{6}}},{T_{1} + {4\frac{3}{6}}}} \right\rbrack,$

[0213] where T₁ is the pitch lag of the previous (1^(st) or 3^(rd))subframe.

[0214] The close-loop pitch search is performed by minimizing themean-square weighted error between the original and synthesized speech.This is achieved by maximizing the term:${{R(k)} = \frac{\sum\limits_{n = 0}^{39}{{T_{gs}(n)}{y_{k}(n)}}}{\sqrt{\sum\limits_{n = 0}^{39}{{y_{k}(n)}{y_{k}(n)}}}}},$

[0215] where T_(gs)(n) is the target signal and y_(k)(n) is the pastfiltered excitation at delay k (past excitation convoluted with h(n)).The convolution y_(k) (n) is computed for the first delay t_(min) in thesearch range, and for the other delays in the search range k=t_(min)+1 ,. . . , t_(max), it is updated using the recursive relation:

y _(k)(n)=y _(k−1)(n−1)+u(−)h(n),

[0216] where u(n),n=−(143+11) to 39 is the excitation buffer.

[0217] Note that in the search stage, the samples u(n),n=0 to 39, arenot available and are needed for pitch delays less than 40. To simplifythe search, the LP residual is copied to u(n) to make the relation inthe calculations valid for all delays. Once the optimum integer pitchdelay is determined, the fractions, as defined above, around thatintegor are tested. The fractional pitch search is performed byinterpolating the normalized correlation and searching for its maximum.

[0218] Once the fractional pitch lag is determined, the adaptivecodebook vector, v(n), is computed by interpolating the past excitationu(n) at the given phase (fraction). The interpolations are performedusing two FIR filters (Hamming windowed sinc functions), one forinterpolating the term in the calculations to find the fractional pitchlag and the other for interpolating the past excitation as previouslydescribed. The adaptive codebook gain, g_(p), is temporally given thenby:${g_{p} = \frac{\sum\limits_{n = 0}^{39}{{T_{gs}(n)}{y(n)}}}{\sum\limits_{n = 0}^{39}{{y(n)}{y(n)}}}},$

[0219] bounded by 0<g_(p)<1.2, where y(n)=v(n)*h(n) is the filteredadaptive codebook vector (zero state response of H(z)W(z) to v(n)). Theadaptive codebook gain could be modified again due to joint optimizationof the gains, gain normalization and smoothing. The term y(n) is alsoreferred to herein as C_(p)(n).

[0220] With conventional approaches, pitch lag maximizing correlationmight result in two or more times the correct one. Thus, with suchconventional approaches, the candidate of shorter pitch lag is favoredby weighting the correlations of different candidates with constantweighting coefficients. At times this approach does not correct thedouble or treble pitch lag because the weighting coefficients are notaggressive enough or could result in halving the pitch lag due to thestrong weighting coefficients.

[0221] In the present embodiment, these weighting coefficients becomeadaptive by checking if the present candidate is in the neighborhood ofthe previous pitch lags (when the previous frames are voiced) and if thecandidate of shorter lag is in the neighborhood of the value obtained bydividing the longer lag (which maximizes the correlation) with aninteger.

[0222] In order to improve the perceptual quality, a speech classifieris used to direct the searching procedure of the fixed codebook (asindicated by the blocks 275 and 279) and tocontrol gain normalization(as indicated in the block 401 of FIG. 4). The speech classifier servesto improve the background noise performance for the lower rate coders,and to get a quick startup of the noise level estimation. The speechclassifier distinguishes stationary noise-like segments from segments ofspeech, music, tonal-like signals, non-stationary noise, etc.

[0223] The speech classification is performed in two steps. An initialclassification (speech_mode) is obtained based on the modified inputsignal. The final classification (exc_mode) is obtained from the initialclassification and the residual signal after the pitch contribution hasbeen removed. The two outputs from the speech classification are theexcitation mode, exc_mode, and the parameter β_(sub)(n), used to controlthe subframe based smoothing of the gains.

[0224] The speech classification is used to direct the encoder accordingto the characteristics of the input signal and need not be transmittedto the decoder. Thus, the bit allocation, codebooks, and decoding remainthe same regardless of the classification. The encoder emphasizes theperceptually important features of the input signal on a subframe basisby adapting the encoding in response to such features. It is importantto notice that misclassification will not result in disastrous speechquality degradations. Thus, as opposed to the VAD 235, the speechclassifier identified within the block 279 (FIG. 2) is designed to besomewhat more aggressive for optimal perceptual quality.

[0225] The initial classifier (speech_classifier) has adaptivethresholds and is performed in six steps:

[0226] 1. Adapt thresholds:

[0227] if (updates_noise≳30 & updates_speech≳30)${SNR\_ max} = {\min \left( {\frac{{ma\_ max}{\_ speech}}{{ma\_ max}{\_ noise}},32} \right)}$

[0228] else

[0229] SNR_max=3.5

[0230] endif

[0231] if (SNR_max <1.75)

[0232] deci_max_mes=1.30

[0233] deci_ma_cp=0.70

[0234] update_max_mes=1.10

[0235] update_ma_(cp)_speech=0.72

[0236] elseif (SNR_max<2.50)

[0237] deci_max_mes=1.65

[0238] deci_ma_cp=0.73

[0239] update_max_mes=1.30

[0240] update_ma_cp_speech=0.72

[0241] else

[0242] deci_max_mes=1.75

[0243] deci_ma_cp=0.77

[0244] update_max_mes=1.30

[0245] update_ma_cp_speech=0.77

[0246] endif

[0247] 2. Calculate parameters:

[0248] Pitch correlation:${cp} = \frac{\sum\limits_{i = 0}^{{L\_ SF} - 1}{{\overset{\sim}{s}(i)} \cdot {\overset{\sim}{s}\left( {i - {lag}} \right)}}}{\sqrt{\left( {\sum\limits_{i = 0}^{{L\_ SF} - 1}{{\overset{\sim}{s}(i)} \cdot {\overset{\sim}{s}(i)}}} \right) \cdot \left( {\sum\limits_{i = 0}^{{L\_ SF} - 1}{{\overset{\sim}{s}\left( {i - {lag}} \right)} \cdot {\overset{\sim}{s}\left( {i - {lag}} \right)}}} \right)}}$

[0249] Running mean of pitch correlation:

ma _(—) cp(n)=0.9·ma _(—) cp(n−1)+0.1·cp

[0250] Maximum of signal amplitude in current pitch cycle:

max(n)=max{|{tilde over (s)}(i){, i=start , . . . , L _(—) SF−1 }

[0251] where:

start=min{L _(—) SF−lag,0 }

[0252] Sum of signal amplitudes in current pitch cycle:${{mean}(n)} = {\sum\limits_{i = {start}}^{{L\_ SF} - 1}\left| {\overset{\sim}{s}(i)} \right|}$

[0253] Measure of relative maximum:${max\_ mes} = \frac{\max (n)}{{ma\_ max}{\_ noise}\left( {n - 1} \right)}$

[0254] Maximum to long-term sum:${\max \quad 2\quad {sum}} = \frac{\max (n)}{\sum\limits_{k = 1}^{14}{{mean}\left( {n - k} \right)}}$

[0255] Maximum in groups of 3 subframes for past 15 subframes:

max_group(n, k)=max{max(n−3·(4−k)−j), j=0 , . . . , 2}, k=b 0 , . . . ,4

[0256] Group-maximum to minimum of previous 4 group-maxima:${{end}\quad \max \quad 2\min \quad \max} = \frac{{max\_ group}\left( {n,4} \right)}{\min \left\{ {{{max\_ group}\left( {n,k} \right)},{k = 0},\ldots \quad,3} \right\}}$

[0257] Slope of 5 group maxima:${slope} = {0.1 \cdot {\sum\limits_{k = 0}^{4}{{\left( {k - 2} \right) \cdot {max\_ group}}\left( {n,k} \right)}}}$

[0258] 3. Classify subframe:

[0259] if (((max_mes<deci_max_mes & ma_cp<deci_ma_cp)|(VAD=0)) &(LTP_MODE=1|5.8kbit/s|4.55kbit/s))

[0260] speech_mode=0/*class1 */

[0261] else

[0262] speech_mode=1/*class2 */

[0263] endif

[0264] 4. Check for change in background noise level, i.e. resetrequired:

[0265] Check for decrease in level:

[0266] if (updates_noise=31 & max_mes<=0.3)

[0267] if (consec_low<15)

[0268] consec_low++

[0269] endif

[0270] else

[0271] consec_low=0

[0272] endif

[0273] if (consec_low=15)

[0274] updates_noise=0

[0275] lev_reset=−1 /* low level reset */

[0276] endif

[0277] Check for increase in level:

[0278] if ((updates_noise>=30|lev_reset=−1) & max_mes>1.5 & ma_cp<0.85 &k1<−0.4 & endmax2minmax<50 & max2sum<35 & slope>−100 & slope<120)

[0279] if (consec_high<15)

[0280] consec_high++

[0281] endif

[0282] else

[0283] consec_high=0

[0284] endif

[0285] if (consec_high=15 & endmax2minmax<6 & max2sum <5))

[0286] updates_noise=30

[0287] lev_reset=1 /* high level reset */

[0288] endif

[0289] 5. Update running mean of maximum of class 1 segments, i.e.stationary noise:

[0290] if(

[0291] /*1. condition: regular update*/

[0292] (max_mes<update_max_mes & ma_cp<0.6 & cp<0.65 & max_mes>0.3)|

[0293] /*2. condition: VAD continued update*/

[0294] (consec_vad_(—)0=8)|

[0295] /*3. condition: start - up/reset update*/

[0296] (updates_noise<30 & ma_cp<0.7 & cp<0.75 & k₁<−0.4 &endmax2minmax<5&

[0297] (lev_reset≠−1|(lev_reset=−1 & max_mes<2)))

[0298] )

[0299] ma_max_noise(n)=0.9·ma_max_noise(n−1)+0.1·max(n)

[0300] if(updates_noise≲30

[0301] updates_noise++

[0302] else

[0303] lev_reset=0

[0304] endif

[0305]

[0306] where k₁ is the first reflection coefficient.

[0307] 6. Update running mean of maximum of class 2 segments, i.e.speech, music, tonal-like signals, non-stationary noise, etc, continuedfrom above:

[0308]

[0309] elseif (ma_cp>update_ma_cp_speech)

[0310] if (updates_speech<80)

[0311] α_(speech)=0.95

[0312] else

[0313] α_(speech)=0.999

[0314] endif

[0315]ma_max_speech(n)=α_(speech)·ma_max_speech(n−1)+(1−α_(speech))·max(n)

[0316] if (updates_speech<80)

[0317] updates_speech++

[0318] endif

[0319] The final classifier (exc_preselect) provides the final class,exc_mode, and the subframe based smoothing parameter, β_(sub) (n). Ithas three steps:

[0320] 1. Calculate parameters:

[0321] Maximum amplitude of ideal excitation in current subframe:

max_(res2)(n)=max{|res2(i)|, i=0 , . . . , L _(—) SF−1}

[0322] Measure of relative maximum:${max\_ mes}_{res2} = \frac{\max_{res2}(n)}{{ma\_ max}_{res2}\left( {n - 1} \right)}$

[0323] 2. Classify subframe and calculate smoothing:

[0324] if (speech_mode=1|max_mes_(res2)≳1.75)

[0325] exc_mode=1/* class 2*/

[0326] β_(sub)(n)=0

[0327] N_mode_sub(n)=−4

[0328] else

[0329] exc_mode=0 /*class 1*/

[0330] N_mode_sub(n)=N_mode_sub(n−1)+1

[0331] if (N_mode_sub(n)>4)

[0332] N_mode_sub(n)=4

[0333] endif

[0334] if (N_mode_sub(n)>0)${\beta_{sub}(n)} = {\frac{0.7}{9} \cdot \left( {{{N\_ mode}{\_ sub}(n)} - 1} \right)^{2}}$

[0335] else

[0336] α_(sub)(n)=0

[0337] endif

[0338] endif

[0339] 3. Update running mean of maximum:

[0340] if (max_mes_(res2)≲0.5)

[0341] if (consec<51)

[0342] consec++

[0343] endif

[0344] else

[0345] consec=0

[0346] endif

[0347] if ((exc_mode=0 & (max_mes_(res2)>0.5|consec>50))|(updates≲30 &ma_cp<0.6 & cp<0.65))

[0348] ma_max(n)=0.9·ma_max(n−1)+0.1·max_(res2)(n)

[0349] if (updates≲30)

[0350] updates++

[0351] endif

[0352] endif

[0353] When this process is completed, the final subframe basedclassification, exc_mode, and the smoothing parameter, β_(sub)(n), areavailable.

[0354] To enhance the quality of the search of the fixed codebook 261,the target signal, T_(g)(n), is produced by temporally reducing the LTPcontribution with a gain factor, G_(r):

T _(g)(n)=T _(gs)(n)−G _(r) *g _(p) *Y _(a)(n), n=0, 1 , . . . , 39

[0355] where T_(gs)(n) is the original target signal 253, Y_(a)(n) isthe filtered signal from the adaptive codebook, g_(p) is the LTP gainfor the selected adaptive codebook vector, and the gain factor isdetermined according to the normalized LTP gain, R_(p), and the bitrate:

[0356] if (rate<=0) /*for4.45 kbps and 5.8 kbps*/

[0357] G_(r)=0.7R_(p)+0.3;

[0358] if (rate==1) /* for 6.65 kbps */

[0359] G_(r)=0.6R_(p)+0.4;

[0360] if (rate==2) /*for 8.0 kbps */

[0361] G_(r)=0.3R_(p)+0.7;

[0362] if (rate==3) /*for 11.0 kbps */

[0363] G_(r)=0.95;

[0364] if (T_(op)>L_SF & g_(p)>0.5 & rate<=2)

[0365] G_(r)<═G_(r)·(0.3R_(p)+0.7);and

[0366] where normalized LTP gain, R_(p), is defined as:$R_{p} = \frac{\sum\limits_{n = 0}^{39}{{T_{gs}(n)}{Y_{a}(n)}}}{\sqrt{\sum\limits_{n = 0}^{39}{{T_{gs}(n)}{T_{gs}(n)}}}\sqrt{\sum\limits_{n = 0}^{39}{{Y_{a}(n)}{Y_{a}(n)}}}}$

[0367] Another factor considered at the control block 275 in conductingthe fixed codebook search and at the block 401 (FIG. 4) during gainnormalization is the noise level +“)” which is given by:$P_{NSR} = \sqrt{\frac{\max \left\{ {\left( {E_{n} - 100} \right),0.0} \right\}}{E_{s}}}$

[0368] where E_(s) is the energy of the current input signal includingbackground noise, and E_(n) is a running average energy of thebackground noise. E_(n) is updated only when the input signal isdetected to be background noise as follows:

[0369] if (first background noise frame is true)

[0370] E_(n)=0.75 E_(s;)

[0371] else if (background noise frame is true)

[0372] E_(n)=0.75 E _(n) _(—) m+0.25 E_(s);

[0373] where E_(n) _(—) m is the last estimation of the background noiseenergy.

[0374] For each bit rate mode, the fixed codebook 261 (FIG. 2) consistsof two or more subcodebooks which are constructed with differentstructure. For example, in the present embodiment at higher rates, allthe subcodebooks only contain pulses. At lower bit rates, one of thesubcodebooks is populated with Gaussian noise. For the lower bit-rates(e.g., 6.65, 5.8, 4.55 kbps), the speech classifier forces the encoderto choose from the Gaussian subcodebook in case of stationary noise-likesubframes, exc_mode=0. For exc_mode=1 all subcodebooks are searchedusing adaptive weighting.

[0375] For the pulse subcodebooks, a fast searching approach is used tochoose a subcodebook and select the code word for the current subframe.The same searching routine is used for all the bit rate modes withdifferent input parameters.

[0376] In particular, the long-term enhancement filter, F_(p)(z), isused to filter through the selected pulse excitation. The filter isdefined as${{F_{p}(z)} = \frac{1}{\left( {1 - {\beta \quad z^{- T}}} \right)}},$

[0377] where T is the integer part of pitch lag at the center of thecurrent subframe, and β is the pitch gain of previous subframe, boundedby [0.2, 1.0]. Prior to the codebook search, the impulsive response h(n)includes the filter F_(p)(z).

[0378] For the Gaussian subcodebooks, a special structure is used inorder to bring down the storage requirement and the computationalcomplexity. Furthermore, no pitch enhancement is applied to the Gaussiansubcodebooks.

[0379] There are two kinds of pulse subcodebooks in the present AMRcoder embodiment. All pulses have the amplitudes of +1 or −1. Each pulsehas 0, 1, 2, 3 or 4 bits to code the pulse position. The signs of somepulses are transmitted to the decoder with one bit coding one sign. Thesigns of other pulses are determined in a way related to the coded signsand their pulse positions.

[0380] In the first kind of pulse subcodebook, each pulse has 3 or 4bits to code the pulse position. The possible locations of individualpulses are defined by two basic non-regular tracks and initial phases:

POS(n _(p) , i)=TRACK(m _(p) , i)+PHAS(n _(p), phas_mode),

[0381] where i=0, 1 , . . . , 7 or 15 (corresponding to 3 or 4 bits tocode the position), is the possible position index, n_(p)=0 , . . . ,N_(p)−1 (N_(p) is the total number of pulses), distinguishes differentpulses, m_(p)=0 or 1, defines two tracks, and phase_mode=0 or 1,specifies two phase modes.

[0382] For 3 bits to code the pulse position, the two basic tracks are:

{TRACK(0,i)}={0, 4, 8, 12, 18, 24, 30, 36}, and

{TRACK(1,i)}={0, 6, 12, 18, 22, 26, 30, 34}.

[0383] If the position of each pulse is coded with 4 bits, the basictracks are:

{TRACK(0,i)}=(0, 2, 4, 6, 8, 10, 12, 14, 17, 20, 23, 26, 29, 32, 35,38}, and

{TRACK(1,i)}=(0, 3, 6, 9, 12, 15, 18, 21, 23, 25, 27, 29, 31, 33, 35,37}.

[0384] The initial phase of each pulse is fixed as:

PHAS(n _(p),0)=modulus(n _(p)/MAXPHAS)

PHAS(n _(p),1)=PHAS(N _(p)−1−n _(p), 0)

[0385] where MAXPHAS is the maximum phase value.

[0386] For any pulse subcodebook, at least the first sign for the firstpulse, SIGN(n_(p)), n_(p)=0, is encoded because the gain sign isembedded. Suppose N_(sign) is the number of pulses with encoded signs;that is, SIGN(n_(p)), for n_(p)<N_(sign),<=N_(p), is encoded whileSIGN(n_(p)), for n_(p)>=N _(signs), is not encoded. Generally, all thesigns can be determined in the following way:

SIGN(n _(p))=−SIGN(n _(p)−1), for n _(p) >=N _(sign),

[0387] due to that the pulse positions are sequentially searched fromn_(p)=0 to n_(p)=N_(p)−1 using an iteration approach. If two pulses arelocated in the same track while only the sign of the first pulse in thetrack is encoded, the sign of the second pulse depends on its positionrelative to the first pulse. If the position of the second pulse issmaller, then it has opposite sign, otherwise it has the same sign asthe first pulse.

[0388] In the second kind of pulse subcodebook, the innovation vectorcontains 10 signed pulses. Each pulse has 0, 1, or 2 bits to code thepulse position. One subframe with the size of 40 samples is divided into10 small segments with the length of 4 samples. 10 pulses arerespectively located into 10 segments. Since the position of each pulseis limited into one segment, the possible locations for the pulsenumbered with n_(p) are, {4n_(p)}, {4n_(p), 4n_(p)2}, or {4n_(p),4n_(p)+1, 4n_(p)+2, 4n_(p)+3}, respectively for 0, 1, or 2 bits to codethe pulse position. All the signs for all the 10 pulses are encoded.

[0389] The fixed codebook 261 is searched by minimizing the mean squareerror between the weighted input speech and the weighted synthesizedspeech. The target signal used for the LTP excitation is updated bysubtracting the adaptive codebook contribution. That is:

x ₂(n)=x(n)−ĝ _(p) y(n), n=0 , . . . , 39,

[0390] where y(n)=v(n)*h(n) is the filtered adaptive codebook vector andĝ_(p) is the modified (reduced) LTP gain.

[0391] If c_(k) is the code vector at index k from the fixed codebook,then the pulse codebook is searched by maximizing the term:${A_{k} = {\frac{\left( C_{k} \right)^{2}}{E_{D_{k}}} = \frac{\left( {d^{t}c_{k}} \right)^{2}}{c_{k}^{t}\Phi \quad c_{k}}}},$

[0392] where d=H^(t)x₂ is the correlation between the target signalx₂(n) and the impulse response h(n), H is a the lower triangular Toeplizconvolution matrix with diagonal h(0) and lower diagonals h(1) , . . . ,h(39), and Φ=H^(t)H is the matrix of correlations of h(n). The vector d(backward filtered target) and the matrix Φ are computed prior to thecodebook search. The elements of the vector d are computed by:${{d(n)} = {\sum\limits_{i = n}^{39}{{x_{2}(i)}{h\left( {i - n} \right)}}}},\quad {n = 0},\ldots \quad,39,$

[0393] and the elements of the symmetric matrix Φ are computed by:${{\varphi \left( {i,j} \right)} = {\sum\limits_{n = j}^{39}{{h\left( {n - i} \right)}{h\left( {n - j} \right)}}}},{\left( {j \geq i} \right).}$

[0394] The correlation in the numerator is given by:${C = {\sum\limits_{i = 0}^{N_{p} - 1}{\vartheta_{i}{d\left( m_{i} \right)}}}},$

[0395] where m_(i) is the position of the i th pulse and θ_(i) is itsamplitude. For the complexity reason, all the amplitudes {θ_(i)} are setto +1 or −1; that is,

θ_(i)=SIGN(i), i=n _(p)=0 , . . . , N _(p)−1.

[0396] The energy in the denominator is given by:$E_{D} = {{\sum\limits_{i = 0}^{N_{p} - 1}{\varphi \left( {m_{i},m_{i}} \right)}} + {2{\sum\limits_{i = 0}^{N_{p} - 2}{\sum\limits_{j = {i + 1}}^{N_{p} - 1}{\vartheta_{i}\vartheta_{j}{{\varphi \left( {m_{i},m_{j}} \right)}.}}}}}}$

[0397] To simplify the search procedure, the pulse signs are preset byusing the signal b(n), which is a weighted sum of the normalized d(n)vector and the normalized target signal of x₂(n) in the residual domainres₂(n):${{b(n)} = {\frac{{res}_{2}(n)}{\sqrt{\sum\limits_{i = 0}^{39}\quad {{{res}_{2}(i)}{{res}_{2}(i)}}}} + \frac{2{d(n)}}{\sqrt{\sum\limits_{i = 0}^{39}\quad {{(i)}{(i)}}}}}},{n = 0},1,\ldots \quad,39$

[0398] If the sign of the i th (i=n_(p)) pulse located at m_(i) isencoded, it is set to the sign of signal b(n) at that position, i.e.,SIGN(i)=sign[b(m_(i))].

[0399] In the present embodiment, the fixed codebook 261 has 2 or 3subcodebooks for each of the encoding bit rates. Of course many moremight be used in other embodiments. Even with several subcodebooks,however, the searching of the fixed codebook 261 is very fast using thefollowing procedure. In a first searching turn, the encoder processingcircuitry searches the pulse positions sequentially from the first pulse(n_(p)=0) to the last pulse (n_(p)=N_(p)−1) by considering the influenceof all the existing pulses.

[0400] In a second searching turn, the encoder processing circuitrycorrects each pulse position sequentially from the first pulse to thelast pulse by checking the criterion value A_(k) contributed from allthe pulses for all possible locations of the current pulse. In a thirdturn, the functionality of the second searching turn is repeated a finaltime. Of course further turns may be utilized if the added complexity isnot prohibitive.

[0401] The above searching approach proves very efficient, because onlyone position of one pulse is changed leading to changes in only one termin the criterion numerator C and few terms in the criterion denominatorE_(D) for each computation of the A_(k). As an example, suppose a pulsesubcodebook is constructed with 4 pulses and 3 bits per pulse to encodethe position. Only 96 (4pulses×2³ positions per pulse×3turns=96)simplified computations of the criterion A_(k) need be performed.

[0402] Moreover, to save the complexity, usually one of the subcodebooksin the fixed codebook 261 is chosen after finishing the first searchingturn. Further searching turns are done only with the chosen subcodebook.In other embodiments, one of the subcodebooks might be chosen only afterthe second searching turn or thereafter should processing resources sopermit.

[0403] The Gaussian codebook is structured to reduce the storagerequirement and the computational complexity. A comb-structure with twobasis vectors is used. In the comb-structure, the basis vectors areorthogonal, facilitating a low complexity search. In the AMR coder, thefirst basis vector occupies the even sample positions, (0,2 , . . . ,38), and the second basis vector occupies the odd sample positions, (1,3, . . . , 39).

[0404] The same codebook is used for both basis vectors, and the lengthof the codebook vectors is 20 samples (half the subframe size).

[0405] All rates (6.65, 5.8 and 4.55 kbps) use the same Gaussiancodebook. The Gaussian codebook, CB_(Gauss), has only 10 entries, andthus the storage requirement is 10·20=200 16-bit words. From the 10entries, as many as 32 code vectors are generated. An index, idx_(δ), toone basis vector 22 populates the corresponding part of a code vector,c_(idx) _(δ) , in the following way:

c _(idx) _(δ) (2·(i−τ)+δ)+CB _(Gauss)(l,i) i=τ,τ+1 , . . . , 19

c _(idx) _(δ) (2·(i+20−τ)+δ)=CB _(Gauss)(l,i) i=0,1 , . . . , τ−1

[0406] where the table entry, l, and the shift, τ, are calculated fromthe index, idx_(θ), according to:

τ=trunc{idx _(θ)/10}

l=idx _(θ)−10 ·τ

[0407] and θ is 0 for the first basis vector and 1 for the second basisvector. In addition, a sign is applied to each basis vector.

[0408] Basically, each entry in the Gaussian table can produce as manyas 20 unique vectors, all with the same energy due to the circularshift. The 10 entries are all normalized to have identical energy of0.5, i.e.,${{\sum\limits_{i = 0}^{19}\quad \left( {{CB}_{Gauss}\left( {l,i} \right)} \right)^{2}} = 0.5},{l = 0},1,\ldots \quad,9$

[0409] That means that when both basis vectors have been selected, thecombined code vector, c_(idx) ₀ _(,idx) ₁ , will have unity energy, andthus the final excitation vector from the Gaussian subcodebook will haveunity energy since no pitch enhancement is applied to candidate vectorsfrom the Gaussian subcodebook.

[0410] The search of the Gaussian codebook utilizes the structure of thecodebook to facilitate a low complexity search. Initially, thecandidates for the two basis vectors are searched independently based onthe ideal excitation, res₂. For each basis vector, the two bestcandidates, along with the respective signs, are found according to themean squared error. This is exemplified by the equations to find thebest candidate, index idx_(θ), and its sign, S_(idx) _(θ) :${idx}_{\delta} = {\max\limits_{{k = 0},1,\ldots \quad,N_{Gauss}}\left\{ {{\sum\limits_{i = 0}^{19}\quad {{{res}_{2}\left( {{2 \cdot i} + \delta} \right)} \cdot {c_{k}\left( {{2 \cdot i} + \delta} \right)}}}} \right\}}$$s_{{idx}_{\delta}} = {{sign}\left( {\sum\limits_{i = 0}^{19}\quad {{{res}_{2}\left( {{2 \cdot i} + \delta} \right)} \cdot {c_{{idx}_{\delta}}\left( {{2 \cdot i} + \delta} \right)}}} \right)}$

[0411] where N_(Gauss) is the number of candidate entries for the basisvector. The remaining parameters are explained above. The total numberof entries in the Gaussian codebook is 2·2·N_(Gauss) ². The fine searchminimizes the error between the weighted speech and the weightedsynthesized speech considering the possible combination of candidatesfor the two basis vectors from the preselection. If c_(k) ₀ _(,k) ₁ isthe Gaussian code vector from the candidate vectors represented by theindices k₀ and k₁ and the respective signs for the two basis vectors,then the final Gaussian code vector is selected by maximizing the term:$A_{k_{0},k_{1}} = {\frac{\left( C_{k_{0},k_{1}} \right)^{2}}{E_{D_{k_{0},k_{1}}}} = \frac{\left( {d^{t}\quad c_{k_{0},k_{1}}} \right)^{2}}{c_{k_{0},k_{1}}^{t}\Phi \quad c_{k_{0},k_{1}}}}$

[0412] over the candidate vectors. d=H^(t)x₂ is the correlation betweenthe target signal x₂(n) and the impulse response h(n) (without the pitchenhancement), and H is a the lower triangular Toepliz convolution matrixwith diagonal h(0) and lower diagonals h(1) , . . . , h(39), andΦ=H^(t)H is the matrix of correlations of h(n).

[0413] More particularly, in the present embodiment, two subcodebooksare included (or utilized) in the fixed codebook 261 with 31 bits in the11 kbps encoding mode. In the first subcodebook, the innovation vectorcontains 8 pulses. Each pulse has 3 bits to code the pulse position. Thesigns of 6 pulses are transmitted to the decoder with 6 bits. The secondsubcodebook contains innovation vectors comprising 10 pulses. Two bitsfor each pulse are assigned to code the pulse position which is limitedin one of the 10 segments. Ten bits are spent for 10 signs of the 10pulses. The bit allocation for the subcodebooks used in the fixedcodebook 261 can be summarized as follows:

Subcodebook1: 8 pulses×3 bits/pulse+6 signs=30 bits

Subcodebook2: 10 pulses×2 bits/pulse+10 signs=30 bits

[0414] One of the two subcodebooks is chosen at the block 275 (FIG. 2)by favoring the second subcodebook using adaptive weighting applied whencomparing the criterion value F1 from the first subcodebook to thecriterion value F2 from the second subcodebook:

[0415] if(W_(c)·F1>F2), the first subcodebook is chosen,

[0416] else, the second subcodebook is chosen,

[0417] where the weighting, 0<W_(c)<=1, is defined as:$W_{c} = \left\{ \begin{matrix}{1.0,\quad {{{if}\quad P_{NSR}} < 0.5},} \\{{1.0 - {0.3{{P_{NSR}\left( {1.0 - {0.5R_{p}}} \right)} \cdot \min}\left\{ {{P_{sharp} + 0.5},1.0} \right\}}},}\end{matrix} \right.$

[0418] P_(NSR) is the background noise to speech signal ratio (i.e., the“noise level” in the block 279), R_(p) is the normalized LTP gain, andP_(sharp) is the sharpness parameter of the ideal excitation res₂(n)(i.e., the “sharpness” in the block 279).

[0419] In the 8 kbps mode, two subcodebooks are included in the fixedcodebook 261 with 20 bits. In the first subcodebook, the innovationvector contains 4 pulses. Each pulse has 4 bits to code the pulseposition. The signs of 3 pulses are transmitted to the decoder with 3bits. The second subcodebook contains innovation vectors having 10pulses. One bit for each of 9 pulses is assigned to code the pulseposition which is limited in one of the 10 segments. Ten bits are spentfor 10 signs of the 10 pulses. The bit allocation for the subcodebookcan be summarized as the following:

Subcodebook1: 4 pulses ×4 bits/pulse +3 signs=19 bits

Subcodebook2: 9 pulses ×1 bits/pulse +1 pulse ×0 bit +10 signs=19 bits

[0420] One of the two subcodebooks is chosen by favoring the secondsubcodebook using adaptive weighting applied when comparing thecriterion value F1 from the first subcodebook to the criterion value F2from the second subcodebook as in the 11 kbps mode. The weighting,0<W_(c)<=1, is defined as:

W _(c)=1.0−0.6P _(NSR)(1.0−0.5R _(P))·min{P _(sharp)+0.5, 1.0}.

[0421] The 6.65kbps mode operates using the long-term preprocessing (PP)or the traditional LTP. A pulse subcodebook of 18 bits is used when inthe PP-mode. A total of 13 bits are allocated for three subcodebookswhen operating in the LTP-mode. The bit allocation for the subcodebookscan be summarized as follows:

[0422] PP-mode:

Subcodebook: 5 pulses ×3 bits/pulse +3 signs=18 bits

[0423] LTP-mode:

Subcodebook1: 3 pulses ×3 bits/pulse +3 signs=12 bits, phase_mode=1,

Subcodebook2: 3 pulses ×3 bits/pulse +2 signs=11 bits, phase_mode=0,

Subcodebook3: Gaussian subcodebook of 11bits.

[0424] One of the 3 subcodebooks is chosen by favoring the Gaussiansubcodebook when searching with LTP-mode. Adaptive weighting is appliedwhen comparing the criterion value from the two pulse subcodebooks tothe criterion value from the Gaussian subcodebook. The weighting,0<W_(c)<=1, is defined as:

W _(c)=1.0−0.9P _(NSR)(1.0−0.5R _(p))·min{P _(sharp)0.5, 1.0},

[0425] if (noise - like unvoiced),W_(c)<═W_(c)·(0.2R_(p)(1.0−P_(sharp))+0.8).

[0426] The 5.8 kbps encoding mode works only with the long-termpreprocessing (PP). Total 14 bits are allocated for three subcodebooks.The bit allocation for the subcodebooks can be summarized as thefollowing:

Subcodebook1: 4 pulses ×3 bits/pulse +1 signs=13 bits, phase_mode=1,

Subcodebook2: 3 pulses ×3 bits/pulse +3 signs=12 bits, phase_mode=0,

Subcodebook3: Gaussian subcodebook of 12 bits.

[0427] One of the 3 subcodebooks is chosen favoring the Gaussiansubcodebook with aaptive weighting applied when comparing the criterionvalue from the two pulse subcodebooks to the criterion value from theGaussian subcodebook. The weighting, 0<W_(c)<=1, is defined as:

W _(c)=1.0−P _(NSR)(1.0−0.5R _(p))·min{P _(sharp)+0.6,1.0},

[0428] if (noise - like unvoiced), W_(c)<═W_(c)·(0.3R_(p)(1.0−P_(sharp))+0.7).

[0429] The 4.55 kbps bit rate mode works only with the long-termpreprocessing (PP). Total 10 bits are allocated for three subcodebooks.The bit allocation for the subcodebooks can be summarized as thefollowing:

Subcodebook1: 2 pulses ×4 bits/pulse +1 signs=9 bits, phase_mode=1,

Subcodebook2: 2 pulses ×3 bits/pulse +2 signs=8 bits, phase_mode=0,

Subcodebook3: Gaussian subcodebook of 8 bits.

[0430] One of the 3 subcodebooks is chosen by favoring the Gaussiansubcodebook with weighting applied when comparing the criterion valuefrom the two pulse subcodebooks to the criterion value from the Gaussiansubcodebook. The weighting, 0<W_(c)<=1, is defined as:

W _(c)=1.0−1.2P _(NSR)(1.0−0.5R _(p))·min{P _(sharp)+0.6, 1.0},

[0431] if (noise - like unvoiced),W_(c)<═W_(c)·(0.6R_(p)(1.0−P_(sharp))+0.4).

[0432] For 4.55, 5.8, 6.65 and 8.0 kbps bit rate encoding modes, a gainre-optimization procedure is performed to jointly optimize the adaptiveand fixed codebook gains, g_(p) and g_(c), respectively, as indicated inFIG. 3, The optimal gains are obtained from the following correlationsgiven by:$g_{p} = \frac{{R_{1}R_{2}} - {R_{3}R_{4}}}{{R_{5}R_{2}} - {R_{3}R_{3}}}$${g_{c} = \frac{R_{4} - {g_{p}R_{3}}}{R_{2}}},$

[0433] where R₁=<{overscore (C)}_(p),{overscore (T)}_(gs)>,R₂=<{overscore (C)}_(c),{overscore (C)}_(c)>, R₃=<{overscore(C)}_(p),{overscore (C)}_(c)>,R₄=<{overscore (C)}_(c),{overscore(T)}_(gs)>, and R₅=<{overscore (C)}_(p),{overscore (C)}_(p)>·{overscore(C)}_(c), {overscore (C)}_(p), and {overscore (T)}_(gs) are filteredfixed codebook excitation, filtered adaptive codebook excitation and thetarget signal for the adaptive codebook search.

[0434] For 11 kbps bit rate encoding, the adaptive codebook gain, g_(p),remains the same as that computed in the closeloop pitch search. Thefixed codebook gain, g_(c), is obtained as:${g_{c} = \frac{R_{6}}{R_{2}}},$

[0435] where R₆=<{overscore (C)}_(c) , {overscore (T)} _(g)>and{overscore (T)}_(g)={overscore (T)}_(gs)−g_(p){overscore (C)}_(p).

[0436] Original CELP algorithm is based on the concept of analysis bysynthesis (waveform matching). At low bit rate or when coding noisyspeech, the waveform matching becomes difficult so that the gains areup-down, frequently resulting in unnatural sounds. To compensate forthis problem, the gains obtained in the analysis by synthesis close-loopsometimes need to be modified or normalized.

[0437] There are two basic gain normalization approaches. One is calledopen-loop approach which normalizes the energy of the synthesizedexcitation to the energy of the unquantized residual signal. Another oneis close-loop approach with which the normalization is done consideringthe perceptual weighting. The gain normalization factor is a linearcombination of the one from the close-loop approach and the one from theopen-loop approach; the weighting coefficients used for the combinationare controlled according to the LPC gain.

[0438] The decision to do the gain normalization is made if one of thefollowing conditions is met: (a) the bit rate is 8.0 or 6.65 kbps, andnoise-like unvoiced speech is true; (b) the noise level P_(NSR) islarger than 0.5; (c) the bit rate is 6.65 kbps, and the noise levelP_(NSR) is larger than 0.2; and (d) the bit rate is 5.8 or 4.45kbps.

[0439] The residual energy, E_(res), and the target signal energy,E_(Tgs), are defined respectively as:$E_{res} = {\sum\limits_{n = 0}^{{L\_ SF} - 1}\quad {{res}^{2}(n)}}$$E_{Tgs} = {\sum\limits_{n = 0}^{{L\_ SF} - 1}{T_{gs}^{2}(n)}}$

[0440] Then the smoothed open-loop energy and the smoothed closed-loopenergy are evaluated by:

[0441] if (first subframe is true)

[0442] Ol_Eg=E_(res)

[0443] else

[0444] Ol_Eg<═β_(sub)·Ol_Eg+(1−β_(sub))E_(res)

[0445] if (first subframe is true)

[0446] Cl_Eg=E_(Tgs)

[0447] else

[0448] Cl_Eg<═β_(sub) ·Cl_Eg+(1−β_(sub))E_(Tgs)

[0449] where β_(sub) is the smoothing coefficient which is determinedaccording to the classification. After having the reference energy, theopen-loop gain normalization factor is calculated:${ol\_ g} = {{MIN}\left\{ {{C_{ol}\sqrt{\frac{Ol\_ Eg}{\sum\limits_{n = 0}^{{L\_ SF} - 1}{v^{2}(n)}}}},\frac{1.2}{g_{p}}} \right\}}$

[0450] where C_(ol) is 0.8 for the bit rate 11.0 kbps, for the otherrates C_(ol) is 0.7, and v(n) is the excitation:

v(n)=v _(a)(n)g _(p) +v _(c)(n)g _(c) , n=0,1 , . . . , L _(—) SF−1.

[0451] where g_(p) and g_(c) are unquantized gains. Similarly, theclosed-loop gain normalization factor is:${Cl\_ g} = {{MIN}\left\{ {{C_{cl}\sqrt{\frac{Cl\_ Eg}{\sum\limits_{n = 0}^{{L\_ SF} - 1}{y^{2}(n)}}}},\frac{1.2}{g_{p}}} \right\}}$

[0452] where C_(cl) is 0.9 for the bit rate 11.0 kbps, for the otherrates C_(cl) is 0.8, and y(n) is the filtered signal (y(n)=v(n)*h(n)):

y(n)=y _(a)(n)g _(p) +y _(c)(n)g _(c) , n=0,1 , . . . , L _(—) SF−1.

[0453] The final gain normalization factor, g_(f), is a combination ofCl_g and Ol_g, controlled in terms of an LPC gain parameter, C_(LPC),

[0454] if (speech is true or the rate is 11 kbps)

[0455] g_(f)=C_(LPC)Ol_g+(1−C_(LPC)Cl)_g

[0456] g_(f)=MAX(1.0, gf)

[0457] g_(f)=MIN(g_(f), 1+C_(LPC))

[0458] if (background noise is true and the rate is smaller than 11kbps)

[0459] g_(f)=1.2MIN{Cl_g, Ol_g}

[0460] where C_(LPC) is defined as:

[0461] C_(LPC)=MIN{sqrt(E_(res)/E_(Tgs)), 0.8}/0.8

[0462] Once the gain normalization factor is deter-mined, theunquantized gains are modified:

g _(p) <═g _(p) ·g _(f)

[0463] For 4.55 ,5.8, 6.65 and 8.0 kbps bit rate encoding, the adaptivecodebook gain and the fixed codebook gain are vector quantized using 6bits for rate 4.55 kbps and 7 bits for the other rates. The gaincodebook search is done by minimizing the mean squared weighted error,Err, between the original and reconstructed speech signals:

Err=||{overscore (T)} _(gs) −g _(p) {overscore (C)} _(p) −g _(c){overscore (C)} _(c)||².

[0464] For rate 11.0 kbps, scalar quantization is performed to quantizeboth the adaptive codebook gain, g_(p), using 4 bits and the fixedcodebook gain, g_(c), using 5 bits each.

[0465] The fixed codebook gain, g_(c), is obtained by MA prediction ofthe energy of the scaled fixed codebook excitation in the followingmanner. Let E(n) be the mean removed energy of the scaled fixed codebookexcitation in (dB) at subframe n be given by:${{E(n)} = {{10{\log \left( {\frac{1}{40}g_{c}^{2}{\sum\limits_{i = 0}^{39}\quad {c^{2}(i)}}} \right)}} - \overset{\_}{E}}},$

[0466] where c(i) is the unscaled fixed codebook excitation, and{overscore (E)}=30 dB is the mean energy of scaled fixed codebookexcitation. The predicted energy is given by:${\overset{\sim}{E}(n)} = {\sum\limits_{i = 1}^{4}\quad {b_{i}{\hat{R}\left( {n - i} \right)}}}$

[0467] where [b₁b₂b₃b₄]=[0.680.580.340.19] are the MA predictioncoefficients and {circumflex over (R)}(n) is the quantized predictionerror at subframe n .

[0468] The predicted energy is used to compute a predicted fixedcodebook gain g_(c) (by substituting E(n) by {tilde over (E)}(n) andg_(c) by g_(c)). This is done as follows. First, the mean energy of theunscaled fixed codebook excitation is computed as:${E_{i} = {10{\log \left( {\frac{1}{40}{\sum\limits_{i = 0}^{39}\quad {c^{2}(i)}}} \right)}}},$

[0469] and then the predicted gain g_(c) is obtained as:

g _(c)=10^((0.05({tilde over (E)}(n)+{overscore (E)}−E) ^(_(i)) ⁾.

[0470] A correction factor between the gain, g_(c), and the estimatedone, g_(c), is given by: γ = g_(c)/g_(c).

[0471] It is also related to the prediction error as:

R(n)=E(n)−{tilde over (E)}(n)=20logγ.

[0472] The codebook search for 4.55, 5.8, 6.65 and 8.0 kbps encoding bitrates consists of two steps. In the first step, a binary search of asingle entry table representing the quantized prediction error isperformed. In the second step, the index Index_(—)1 of the optimum entrythat is closest to the unquantized prediction error in mean square errorsense is used to limit the search of the two-dimensional VQ tablerepresenting the adaptive codebook gain and the prediction error. Takingadvantage of the particular arrangement and ordering of the VQ table, afast search using few candidates around the entry pointed by Index_(—)1is performed. In fact, only about half of the VQ table entries aretested to lead to the optimum entry with Index_(—)2. Only Index_(—)2 istransmitted.

[0473] For 11.0 kbps bit rate encoding mode, a full search of bothscalar gain codebooks are used to quantize g_(p) and g_(c). For g_(p),the search is performed by minimizing the error Err=abs(g_(p)−{overscore(g)}_(p)) Whereas for g_(c), the search is performed by minimizing theerror Err=||{overscore (T)}_(gs)−{overscore (g)}_(p){overscore(C)}_(p)−g_(c){overscore (C)}_(c)||².

[0474] An update of the states of the synthesis and weighting filters isneeded in order to compute the target signal for the next subframe.After the two gains are quantized, the excitation signal, u(n), in thepresent subframe is computed as:

u(n)={overscore (g)} _(p) v(n)+{overscore (g)} _(c) c(n),n=0,39,

[0475] where {overscore (g)}_(p) and {overscore (g)}_(c) are thequantized adaptive and fixed codebook gains respectively, v(n) theadaptive codebook excitation (interpolated past excitation), and c(n) isthe fixed codebook excitation. The state of the filters can be updatedby filtering the signal r(n)−u(n) through the filters 1/{overscore(A)}(z) and W(z) for the 40-sample subframe and saving the states of thefilters. This would normally require 3 filterings.

[0476] A simpler approach which requires only one filtering is asfollows. The local synthesized speech at the encoder, ŝ(n), is computedby filtering the excitation signal through 1/{overscore (A)}(z) . Theoutput of the filter due to the input r(n)−u(n) is equivalent toe(n)=s(n)−ŝ(n), so the states of the synthesis filter 1/{overscore(A)}(z) are given by e(n), n=0,39. Updating the states of the filterW(z) can be done by filtering the error signal e(n) through this filterto find the perceptually weighted error e_(w)(n). However, the signale_(w)(n) can be equivalently found by:

e _(w)(n)=T _(gs)(n)−{overscore (g)} _(p) C _(p)(n)−{overscore (g)} _(c)C _(c)(n).

[0477] The states of the weighting filter are updated by computinge_(w)(n) for n=30 to 39.

[0478] The function of the decoder consists of decoding the transmittedparameters (dLP parameters, adaptive codebook vector and its gain, fixedcodebook vector and its gain) and performing synthesis to obtain thereconstructed speech. The reconstructed speech is then postfiltered andupscaled.

[0479] The decoding process is performed in the following order. First,the LP filter parameters are encoded. The received indices of LSFquantization are used to reconstruct the quantized LSF vector.Interpolation is performed to obtain 4 interpolated LSF vectors(corresponding to 4 subframes). For each subframe, the interpolated LSFvector is converted to LP filter coefficient domain, a_(k), which isused for synthesizing the reconstructed speech in the subframe.

[0480] For rates 4.55, 5.8 and 6.65 (during PP_mode) kbps bit rateencoding modes, the received pitch index is used to interpolate thepitch lag across the entire subframe. The following three steps arerepeated for each subframe:

[0481] 1) Decoding of the gains: for bit rates of 4.55, 5.8, 6.65 and8.0 kbps, the received index is used to find the quantized adaptivecodebook gain, {overscore (g)}_(p), from the 2-dimensional VQ table. Thesame index is used to get the fixed codebook gain correction factor γfrom the same quantization table. The quantized fixed codebook gain,{overscore (g)}_(c), is obtained following these steps:

[0482] the predicted energy is computed${{\overset{\sim}{E}(n)} = {\sum\limits_{i = 1}^{4}\quad {b_{i}{\hat{R}\left( {n - i} \right)}}}};$

[0483] the energy of the unscaled fixed codebook excitation iscalculated as${E_{i} = {10{\log \left( {\frac{1}{40}{\sum\limits_{i = 0}^{39}\quad {c^{2}(i)}}} \right)}}};$

[0484] and

[0485] the predicted gain g_(c) is obtained asg_(c)=10^((0.05({tilde over (E)}(n)+{overscore (E)}−E) ^(_(i)) ⁾. Thequantized fixed codebook gain is given as {overscore (g)}_(c)={overscore(γ)}g_(c). For 11 kbps bit rate, the received adaptive codebook gainindex is used to readily find the quantized adaptive gain, {overscore(g)}_(p) from the quantization table. The received fixed codebook gainindex gives the fixed codebook gain correction factor γ. The calculationof the quantized fixed codebook gain, {overscore (g)}_(c) follows thesame steps as the other rates.

[0486] 2) Decoding of adaptive codebook vector: for 8.0 ,11.0 and 6.65(during LTP_mode=1) kbps bit rate encoding modes, the received pitchindex (adaptive codebook index) is used to find the integer andfractional parts of the pitch lag. The adaptive codebook v(n) is foundby interpolating the past excitation u(n) (at the pitch delay) using theFIR filters.

[0487] 3) Decoding of fixed codebook vector: the received codebookindices are used to extract the type of the codebook (pulse or Gaussian)and either the amplitudes and positions of the excitation pulses or thebases and signs of the Gaussian excitation. In either case, thereconstructed fixed codebook excitation is given as c(n) . If theinteger part of the pitch lag is less than the subframe size 40 and thechosen excitation is pulse type, the pitch sharpening is applied. Thistranslates into modifying c(n) as c(n)=c(n)+βc(n−T), where β is thedecoded pitch gain {overscore (g)}_(p) from the previous subframebounded by [0.2,1.0].

[0488] The excitation at the input of the synthesis filter is given byu(n)={overscore (g)}_(p)v(n)+{overscore (g)}_(c)c(n),n=0,39. Before thespeech synthesis, a post-processing of the excitation elements isperformed. This means that the total excitation is modified byemphasizing the contribution of the adaptive codebook vector:${\overset{\_}{u}(n)} = \left\{ \begin{matrix}{{{u(n)} + {0.25\beta {\overset{\_}{g}}_{p}{v(n)}}},} & {{\overset{\_}{g}}_{p} > 0.5} \\{{u(n)},} & {{\overset{}{g}}_{p}<=0.5}\end{matrix} \right.$

[0489] Adaptive gain control (AGC) is used to compensate for the gaindifference between the unemphasized excitation u(n) and emphasizedexcitation {overscore (u)}(n). The gain scaling factor η for theemphasized excitation is computed by: $\eta = \left\{ \begin{matrix}\sqrt{\frac{\sum\limits_{n = 0}^{39}\quad {u^{2}(n)}}{\sum\limits_{n = 0}^{39}{{\overset{\_}{u}}^{2}(n)}}} & {{\overset{\_}{g}}_{p} > 0.5} \\1.0 & {{\overset{}{g}}_{p}<=0.5}\end{matrix} \right.$

[0490] The gain-scaled emphasized excitation {overscore (u)}(n) is givenby:

{overscore (u)}(n)=η{overscore (u)}(n).

[0491] The reconstructed speech is given by:${{\overset{\_}{s}(n)} = {{\overset{\_}{u}(n)} - {\sum\limits_{i = 1}^{10}\quad {{\overset{\_}{a}}_{i}{\overset{\_}{s}\left( {n - i} \right)}}}}},{n = {0\quad {to}\quad 39}},$

[0492] where {overscore (a)}_(i) are the interpolated LP filtercoefficients. The synthesized speech {overscore (s)}(n) is then passedthrough an adaptive postfilter.

[0493] Post-processing consists of two functions: adaptive postfilteringand signal up-scaling. The adaptive postfilter is the cascade of threefilters: a formant postfilter and two tilt compensation filters. Thepostfilter is updated every subframe of 5 ms. The formant postfilter isgiven by:${H_{f}(z)} = \frac{\overset{\_}{A}\left( {z/\gamma_{n}} \right)}{\overset{\_}{A}\left( {z/\gamma_{d}} \right)}$

[0494] where {overscore (A)}(z) is the received quantized andinterpolated LP inverse filter and γ_(n) and γ_(d) control the amount ofthe formant postfiltering.

[0495] The first tilt compensation filter H_(t1)(z) compensates for thetilt in the formant postfilter H_(f)(z) and is given by:

H _(t1)(z)=(1−μz⁻¹)

[0496] where μ=γ_(t1)k₁ is a tilt factor, with k₁ being the firstreflection coefficient calculated on the truncated impulse responseh_(f)(n), of the formant postfilter$k_{1} = \frac{r_{h}(1)}{r_{h}(0)}$

[0497] with:${{r_{h}(i)} = {\sum\limits_{j = 0}^{L_{h} - i - 1}{{h_{f}(j)}{h_{f}\left( {j + i} \right)}}}},{\left( {L_{h} = 22} \right).}$

[0498] The postfiltering process is performed as follows. First, thesynthesized speech {overscore (s)}(n) is inverse filtered through$\overset{\_}{A}\left( {z/\gamma_{n}} \right)$

[0499] to produce the residual signal {overscore (r)}(n). The signal{overscore (r)}(n) is filtered by the synthesis filter 1/{overscore(A)}(z/γ_(d)) is passed to the first tilt compensation filter h_(t1)(z)resulting in the postfiltered speech signal {overscore (s)}_(f)(n).

[0500] Adaptive gain control (AGC) is used to compensate for the gaindifference between the synthesized speech signal {overscore (s)}(n) andthe postfiltered signal {overscore (s)}_(f)(n). The gain scaling factorγ for the present subframe is computed by:$\gamma = \sqrt{\frac{\sum\limits_{n = 0}^{39}{{\overset{\_}{s}}^{2}(n)}}{\sum\limits_{n = 0}^{39}{{\overset{\_}{s}}_{f}^{2}(n)}}}$

[0501] The gain-scaled postfiltered signal {overscore (s)}(n) is givenby:

{overscore (s)}(n)=β(n){overscore (s)} _(f)(n)

[0502] where β(n) is updated in sample by sample basis and given by:

β(n)=αβ(n−1)+(1−α)γ

[0503] where a is an AGC factor with value 0.9. Finally, up-scalingconsists of multiplying the postfiltered speech by a factor 2 to undothe down scaling by 2 which is applied to the input signal.

[0504]FIGS. 6 and 7 are drawings of an alternate embodiment of a 4 kbpsspeech codec that also illustrates various aspects of the presentinvention. In particular, FIG. 6 is a block diagram of a speech encoder601 that is built in accordance with the present invention. The speechencoder 601 is based on the analysis-by-synthesis principle. To achievetoll quality at 4 kbps, the speech encoder 601 departs from the strictwaveform-matching criterion of regular CELP coders and strives to catchthe perceptual important features of the input signal.

[0505] The speech encoder 601 operates on a frame size of 20 ms withthree subframes (two of 6.625 ms and one of 6.75 ms). A look-ahead of 15ms is used. The one-way coding delay of the codec adds up to 55 ms.

[0506] At a block 615, the spectral envelope is represented by a 10thorder LPC analysis for each frame. The prediction coefficients aretransformed to the Line Spectrum Frequencies (LSFs) for quantization.The input signal is modified to better fit the coding model without lossof quality. This processing is denoted “signal modification” asindicated by a block 621. In order to improve the quality of thereconstructed signal, perceptual important features are estimated andemphasized during encoding.

[0507] The excitation signal for an LPC synthesis filter 625 is buildfrom the two traditional components: 1) the pitch contribution; and 2)the innovation contribution. The pitch contribution is provided throughuse of an adaptive codebook 627. An innovation codebook 629 has severalsubcodebooks in order to provide robustness against a wide range ofinput signals. To each of the two contributions a gain is applied which,multiplied with their respective codebook vectors and summed, providethe excitation signal.

[0508] The LSFs and pitch lag are coded on a frame basis, and theremaining parameters (the innovation codebook index, the pitch gain, andthe innovation codebook gain) are coded for every subframe. The LSFvector is coded using predictive vector quantization. The pitch lag hasan integer part and a fractional part constituting the pitch period. Thequantized pitch period has a non-uniform resolution with higher densityof quantized values at lower delays. The bit allocation for theparameters is shown in the following table. Table of Bit AllocationParameter Bits per 20 ms LSFs 21 Pitch lag (adaptive codebook)  8 Gains12 Innovation codebook 3 × 13 = 39 Total 80

[0509] When the quantization of all parameters for a frame is completethe indices are multiplexed to form the 80 bits for the serialbit-stream.

[0510]FIG. 7 is a block diagram of a decoder 701 with correspondingfunctionality to that of the encoder of FIG. 6. The decoder 701 receivesthe 80 bits on a frame basis from a demultiplexor 711. Upon receipt ofthe bits, the decoder 701 checks the sync-word for a bad frameindication, and decides whether the entire 80 bits should be disregardedand frame erasure concealment applied. If the frame is not declared aframe erasure, the 80 bits are mapped to the parameter indices of thecodec, and the parameters are decoded from the indices using the inversequantization schemes of the encoder of FIG. 6.

[0511] When the LSFs, pitch lag, pitch gains, innovation vectors, andgains for the innovation vectors are decoded, the excitation signal isreconstructed via a block 715. The output signal is synthesized bypassing the reconstructed excitation signal through an LPC synthesisfilter 721 To enhance the perceptual quality of the reconstructed signalboth short-term and long-term post-processing are applied at a block731.

[0512] Regarding the bit allocation of the 4 kbps codec (as shown in theprior table), the LSFs and pitch lag are quantized with 21 and 8 bitsper 20 ms, respectively. Although the three subframes are of differentsize the remaining bits are allocated evenly among them. Thus, theinnovation vector is quantized with 13 bits per subframe. This adds upto a total of 80 bits per 20 ms, equivalent to 4 kbps.

[0513] The estimated complexity numbers for the proposed 4 kbps codecare listed in the following table. All numbers are under the assumptionthat the codec is implemented on commercially available 16-bit fixedpoint DSPs in full duplex mode. All storage numbers are under theassumption of 16-bit words, and the complexity estimates are based onthe floating point C-source code of the codec. Table of ComplexityEstimates Computational complexity 30 MIPS Program and data ROM 18kwords RAM  3 kwords

[0514] The decoder 701 comprises decode processing circuitry thatgenerally operates pursuant to software control. Similarly, the encoder601 (FIG. 6) comprises encoder processing circuitry also operatingpursuant to software control. Such processing circuitry may coexists, atleast in part, within a single processing unit such as a single DSP.

[0515]FIG. 8 is a diagram illustrating a codebook built in accordancewith the present invention in which each entry therein is used togenerate a plurality of codevectors. Specifically, a first codebook 811comprises a table of codevectors V₀ 813 through V_(L) 817, that is,codevectors V₀, V₁, . . . , V_(L−1), V_(L). A given codevector CX(N)contains pulse definitions C₀, C₁, C₂, C₃ . . . , C_(N−1), C_(N).

[0516] An initial sequence each of the codevector entries in thecodebook 811 are selected to have a normalized energy level of one, tosimplify search processing. Each of the codevector entries in thecodebook 811 are used to generate a plurality of excitation vectors.With N-1 shifts as illustrated by the bit positions 821, 823, 825 and829, each codebook entry can generate N-1 different excitation vectors,each having the normalized energy of one.

[0517] More particularly, an initial shift of one each for each of theelements (pulse definitions) of the codevector entry generates anadditional excitation vector 823. A further one bit shift generatescodevector 825. Finally, the (N−1)th codevector 829 is generated, thatis, the last unique excitation vector before an additional bit shiftreturns the bits to the position of the initial excitation vector 821.Thus, with less storage space, a single normalized entry can be used aplurality of times in an arrangement that greatly benefits in searchingspeed because each of the resultant vectors will have a normalizedenergy value of one. Such shifting may also be referred to as unwrappingor unfolding.

[0518]FIG. 9 is an illustration of an alternate embodiment of thepresent invention demonstrating that the shifting step may be more thanone. Again, codebook 911 comprises a table of codevectors V₀ 913 throughV_(L) 917, that is codevectors V₀, V₁, . . . , V_(L−1), V_(L), thereinthe codevector C_(x(N)) contains bits C₀, C₁, C₂, C₃, . . . , C_(N−1),C_(N).

[0519] After initial codevector 921 is specified, an additionalcodevector 925 isgenerated by shifting the codevector elements (i.e.,pulse definitions) by two at a time. Further shifting of the codevectorbits generates additional codevectors until the (N-2)^(th) codevector927 is generated. Additional codevectors can be generated by shiftingthe initially specified codevector by any number of bits, theoreticallyfrom one to N−1 bits.

[0520]FIG. 10 is an illustration of an alternate embodiment of thepresent invention demonstrating a pseudo-random population from a singlecodevector entry to generate a pluraliyt of codevectors therefrom. Inparticular, from a codevector 1021 a pseudo-random population of aplurality of new codevectors may be generated from each single codebookentry. A seed value for the population can be shared by both the encoderand decoder, and possibly used as a mechanism for at least low levelencryption.

[0521] Although the unfolding or unwrapping of a single entry may beonly as needed during codebook searching, such processing may take placeduring the generation of a particular codebook itself. Additionally, ascan be appreciated with reference to the searching processes set forthabove, further benefits can be appreciated in ease and speed ofsearching using normalized excitation vectors.

[0522] Of course, many other modifications and variations are alsopossible. In view of the above detailed description of the presentinvention and associated drawings, such other modifications andvariations will now become apparent to those skilled in the art. Itshould also be apparent that such other modifications and variations maybe effected without departing from the spirit and scope of the presentinvention.

[0523] In addition, the following Appendix A provides a list of many ofthe definitions, symbols and abbreviations used in this application.Appendices B and C respectively provide source and channel bit orderinginformation at various encoding bit rates used in one embodiment of thepresent invention. Appendices A, B and C comprise part of the detaileddescription of the present application, and, otherwise, are herebyincorporated herein by reference in its entirety.

I claim:
 1. A speech encoder using a system of codebook vectors as anexcitation signal in speech coding, the speech encoder comprising: afirst codebook comprising a first plurality of codevectors, each of theplurality of codevectors defining a plurality of pulses; and an encoderprocessing circuit, coupled to the first codebook, that rearranges theplurality of pulses in each of the plurality of codevectors to generatea second plurality of codevectors.
 2. The speech encoder of claim 1wherein the plurality of pulses are rearranged by shifting.
 3. Thespeech encoder of claim 1 wherein the second plurality of codevectorsare specified by shifting at least two pulses at a time.
 4. The speechencoder of claim 1 wherein the second plurality of codevectors arespecified through random population.
 5. The speech encoder of claim 1wherein at least one of the first plurality of codevectors is normalizedto an energy level of one.
 6. A speech encoder using a system ofcodebook vectors as an excitation signal in speech coding, the speechencoder comprising: a starting codevector defining a plurality ofpulses; a plurality of resultant codevectors; and an encoder processingcircuit that accesses the starting codevector to generate the pluralityof resultant codevectors by rearranges the plurality of pulses in eachof the plurality of codevectors to generate a second plurality ofcodevectors.
 7. The speech encoder of claim 6 wherein the rearrangingcomprises shifting.
 8. The speech encoder of claim 6 wherein therearranging comprises shifting by at least two pulses at a time.
 9. Thespeech encoder of claim 6 wherein the rearranging occurs through randompopulation.
 10. The speech encoder of claim 6 wherein the startingcodevector is normalized.
 11. A method used by a speech encoder, themethod comprising: selecting a plurality of codebooks comprising aplurality of codevectors, the codevectors each being set to a normalizedenergy level of one and comprising a plurality of pulses; and generatinga plurality of additional codevectors by shifting the bits of thecodevector.
 12. The method of claim 11 further comprising shifting atleast one of the plurality of pulses.